fix: use keyword args for Qwen VL processor call (#58)

* fix: replace AutoModelForVision2Seq with AutoModelForImageTextToText for transformers 5.x

AutoModelForVision2Seq was removed in transformers 5.x (shipped on AWS DL AMI).
Use AutoModelForImageTextToText as the primary import with a fallback to
AutoModelForVision2Seq for older transformers versions.

Files updated:
- openadapt_ml/training/grpo/trainer.py
- openadapt_ml/cloud/modal_cloud.py
- docs/grpo_trl_rewrite_draft.py (comment only)

Note: openadapt_ml/training/trl_trainer.py already had the correct
try/except pattern and was not modified.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

* fix: use keyword args for Qwen VL processor to avoid positional conflict

Qwen2_5_VLProcessor.__call__() expects text= and images= as keyword
args. Passing text as positional arg conflicts with images kwarg:
TypeError: got multiple values for argument 'images'

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: abrichr

docs/grpo_trl_rewrite_draft.py

@@ -952,7 +952,7 @@ def train_grpo(waa_config: WAATrainingConfig | None = None) -> str:
 # to verify this works with Unsloth's patched model loading.
 #
 # If incompatible, we can:
-#   (a) Use standard HF model loading (AutoModelForVision2Seq)
+#   (a) Use standard HF model loading (AutoModelForImageTextToText)
 #   (b) Load with Unsloth, then pass the model to TRL
 #   (c) Use Unsloth's GRPOTrainer fork (if available)
 #

openadapt_ml/cloud/modal_cloud.py

@@ -336,9 +336,12 @@ def infer(
         if not hasattr(infer, "_model"):
             print(f"Loading base model: {_base}")
             try:
-                from transformers import AutoModelForVision2Seq
+                try:
+                    from transformers import AutoModelForImageTextToText as AutoVLM
+                except ImportError:
+                    from transformers import AutoModelForVision2Seq as AutoVLM
 
-                infer._model = AutoModelForVision2Seq.from_pretrained(
+                infer._model = AutoVLM.from_pretrained(
                     _base,
                     torch_dtype=torch.bfloat16,
                     device_map="auto",

openadapt_ml/training/grpo/trainer.py

@@ -23,7 +23,7 @@
     - beta=0.0 (no KL penalty) per DAPO/Open-Reasoner-Zero. Simpler, saves
       memory (no reference model needed).
     - Per-step backward to avoid OOM on long trajectories.
-    - Standard HF model loading: AutoModelForVision2Seq + AutoProcessor + PEFT.
+    - Standard HF model loading: AutoModelForImageTextToText + AutoProcessor + PEFT.
     - Standard PEFT checkpointing: model.save_pretrained().
 """
 
@@ -222,7 +222,12 @@ def _load_model_and_processor(config: GRPOConfig) -> tuple[Any, Any]:
         (model, processor) tuple. Model has LoRA adapters attached.
     """
     from peft import LoraConfig, PeftModel, get_peft_model
-    from transformers import AutoModelForVision2Seq, AutoProcessor
+    from transformers import AutoProcessor
+
+    try:
+        from transformers import AutoModelForImageTextToText as AutoVLM
+    except ImportError:
+        from transformers import AutoModelForVision2Seq as AutoVLM
 
     processor = AutoProcessor.from_pretrained(config.model_name)
 
@@ -239,7 +244,7 @@ def _load_model_and_processor(config: GRPOConfig) -> tuple[Any, Any]:
             bnb_4bit_quant_type="nf4",
         )
 
-    model = AutoModelForVision2Seq.from_pretrained(config.model_name, **load_kwargs)
+    model = AutoVLM.from_pretrained(config.model_name, **load_kwargs)
 
     if config.lora_checkpoint:
         logger.info("Loading existing LoRA from %s", config.lora_checkpoint)
@@ -322,7 +327,7 @@ def agent_fn(obs: Any) -> BenchmarkAction:
             else:
                 text_input = messages[-1]["content"]
 
-            inputs = processor(text_input, images=[image], return_tensors="pt")
+            inputs = processor(text=[text_input], images=[image], return_tensors="pt")
             inputs = {k: v.to(model.device) for k, v in inputs.items()}
 
             with torch.no_grad():
@@ -519,7 +524,7 @@ def _compute_rollout_loss(
                 text_input = messages[-1]["content"]
 
             prompt_inputs = self._processor(
-                text_input, images=[image], return_tensors="pt"
+                text=[text_input], images=[image], return_tensors="pt"
             )
             prompt_ids = prompt_inputs["input_ids"]
             prompt_len = prompt_ids.shape[1]