支持interlm25,支持模型对齐

paddleformers/cli/utils/llm_utils.py

@@ -420,6 +420,14 @@ def get_lora_target_modules(model):
             "model.visual.blocks.*mlp.up_proj.*",
             "model.visual.blocks.*mlp.down_proj.*",
         ]
+    elif model.config.model_type == "internlm2_5":
+        target_modules = [
+            ".*wqkv.*",
+            ".*wo.*",
+            ".*w1.*",
+            ".*w2.*",
+            ".*w3.*",
+        ]
     else:
         raise ValueError(f"Unknown base_model_prefix: {model.config.model_type}.")
     return target_modules

paddleformers/datasets/template/template.py

@@ -985,3 +985,13 @@ def _get_gpt_oss_prefix():
     chat_sep="<|assistant|>\n",
     mm_plugin=get_mm_plugin(name="glm_ocr", image_token="<|image|>"),
 )
+register_template(
+    name="internlm2_5",
+    format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
+    format_assistant=StringFormatter(slots=["{{content}}<|im_end|>\n"]),
+    format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
+    format_prefix=EmptyFormatter(slots=["<s>"]),
+    chat_sep="<|im_end|>\n",
+    suffix=["<|im_end|>\n"],
+    enable_thinking=None,
+)

paddleformers/transformers/__init__.py

@@ -333,6 +333,17 @@
     ],
     "glm_ocr.processor": ["Glm46VProcessor"],
     "glm_ocr.image_processor": ["Glm46VImageProcessor"],
+    "intern_lm2_5.configuration": ["InternLM25Config"],
+    "intern_lm2_5.modeling": [
+        "InternLM25DecoderLayer",
+        "InternLM25Model",
+        "InternLM25ForCausalLM",
+        "InternLM25PretrainedModel",
+        "InternLM25ForSequenceClassification",
+        "InternLM25ForQuestionAnswering",
+        "InternLM25ForTokenClassification",
+    ],
+    "intern_lm2_5.tokenizer": ["InternLM25Tokenizer"],
 }
 
 if TYPE_CHECKING:
@@ -410,6 +421,7 @@
     from .phi3 import *
     from .gemma3_text import *
     from .glm_ocr import *
+    from .intern_lm2_5 import *
 else:
     sys.modules[__name__] = _LazyModule(
         __name__,

paddleformers/transformers/auto/configuration.py

@@ -61,6 +61,7 @@
         ("glm_ocr", "GlmOcrConfig"),
         ("qwen3_5", "Qwen3_5Config"),
         ("qwen3_5_moe", "Qwen3_5MoEConfig"),
+        ("internlm2", "InternLM2Config"),
     ]
 )
 
@@ -89,6 +90,7 @@
         ("glm_ocr", "GlmOcrForConditionalGeneration"),
         ("qwen3_5_moe", "Qwen3_5MoEForConditionalGeneration"),
         ("qwen3_5", "Qwen3_5ForConditionalGeneration"),
+        ("internlm2", "InternLM2"),
     ]
 )
 
@@ -102,6 +104,7 @@
         ("qwen2_5_vl_text", "qwen2_5_vl"),
         ("qwen3_vl_text", "qwen3_vl"),
         ("qwen3_vl_moe_text", "qwen3_vl_moe"),
+        ("internlm2", "intern"),
     ]
 )
 

paddleformers/transformers/auto/modeling.py

@@ -78,6 +78,7 @@
         ("Gemma3", "gemma3_text"),
         ("Glm4vMoe", "glm4v_moe"),
         ("GlmOcr", "glm_ocr"),
+        ("InternLM2", "intern"),
     ]
 )
 

paddleformers/transformers/intern/__init__.py

@@ -0,0 +1,44 @@
+# Copyright (c) 2026 PaddlePaddle Authors. 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.
+
+"""
+InternLM2 Common Module
+
+This module provides unified access to both InternLM2 2.0 and 2.5 models.
+It automatically routes to the correct implementation based on the model configuration.
+"""
+
+from .configuration import InternLM2Config
+from .modeling import (
+    InternLM2ForCausalLM,
+    InternLM2ForQuestionAnswering,
+    InternLM2ForSequenceClassification,
+    InternLM2ForTokenClassification,
+    InternLM2Model,
+    InternLM2PretrainedModel,
+)
+
+# Alias for auto system compatibility
+InternLM2 = InternLM2Model
+
+__all__ = [
+    "InternLM2Config",
+    "InternLM2Model",
+    "InternLM2",
+    "InternLM2PretrainedModel",
+    "InternLM2ForCausalLM",
+    "InternLM2ForSequenceClassification",
+    "InternLM2ForQuestionAnswering",
+    "InternLM2ForTokenClassification",
+]

paddleformers/transformers/intern/configuration.py

@@ -0,0 +1,126 @@
+# Copyright (c) 2026 PaddlePaddle Authors. 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.
+
+"""
+InternLM2 Common Configuration
+
+This module provides a unified configuration for both InternLM2 2.0 and 2.5 models.
+It detects the version based on the configuration fields and routes accordingly.
+"""
+
+from paddleformers.transformers.configuration_utils import PretrainedConfig
+
+
+class InternLM2Config(PretrainedConfig):
+    """
+    InternLM2 configuration. This is a unified config that handles both 2.0 and 2.5 versions.
+
+    When loading from HuggingFace, the `model_type` will be "internlm2" (not "internlm2_5").
+    This config detects the actual version and routes to the appropriate implementation.
+    """
+
+    model_type = "internlm2"  # Important: must match HuggingFace config
+    _auto_class = "AutoConfig"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=92550,
+        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=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        bias=True,
+        rope_theta=10000,
+        rope_scaling=None,
+        attn_implementation=None,
+        dtype="bfloat16",
+        **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.bias = bias
+
+        import paddle
+
+        if isinstance(dtype, str):
+            dtype_map = {
+                "float32": paddle.float32,
+                "float16": paddle.float16,
+                "bfloat16": paddle.bfloat16,
+            }
+            self.dtype = dtype_map.get(dtype.lower(), paddle.float32)
+        else:
+            self.dtype = dtype
+
+        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.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = "eager"
+
+        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,
+        )
+
+    def _rope_scaling_validation(self):
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_factor is None:
+            raise ValueError("`rope_scaling` must contain 'type' and 'factor' keys, " f"got {self.rope_scaling}")
+        if rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(f"`rope_scaling` type must be 'linear' or 'dynamic', got '{rope_scaling_type}'")
+
+    @property
+    def is_version_2_5(self):
+        if hasattr(self, "auto_map") and self.auto_map is not None:
+            if "AutoModelForSequenceClassification" in self.auto_map:
+                return True
+        return False