fix: defensively rename VLM chat column to match model's forward() signature

studio/backend/core/training/trainer.py

@@ -425,6 +425,7 @@ class UnslothTrainer:
                 dataset,
                 model_name=self.model_name,
                 tokenizer=self.tokenizer,
+                model=self.model,
                 is_vlm=self.is_vlm,
                 format_type=format_type,
                 dataset_name=dataset_source,
@@ -447,6 +448,7 @@ class UnslothTrainer:
                     eval_dataset,
                     model_name=self.model_name,
                     tokenizer=self.tokenizer,
+                    model=self.model,
                     is_vlm=self.is_vlm,
                     format_type=format_type,
                     dataset_name=dataset_source,

studio/backend/utils/datasets/dataset_utils.py

@@ -28,6 +28,8 @@ from .format_conversion import (
     convert_alpaca_to_chatml,
     convert_to_vlm_format,
     convert_llava_to_vlm_format,
+    get_expected_chat_column,
+    rename_chat_column_in_list,
 )
 from .chat_templates import (
     apply_chat_template_to_dataset,
@@ -547,6 +549,7 @@ def format_and_template_dataset(
     dataset,
     model_name,
     tokenizer,
+    model=None,
     is_vlm = False,
     format_type="auto",
     # VLM-specific parameters
@@ -735,12 +738,30 @@ def format_and_template_dataset(
             dataset = [sample for sample in dataset]
             warnings.append("Dataset already in standard VLM messages format")
 
+        # Defensive: rename chat column if model expects a different name
+        expected_col = get_expected_chat_column(model) if model is not None else None
+        # VLM data is a list of dicts — check what key the first item uses
+        current_col = "messages"  # default from our converters
+        if isinstance(dataset, list) and len(dataset) > 0:
+            sample_keys = dataset[0].keys()
+            if "conversations" in sample_keys:
+                current_col = "conversations"
+            elif "messages" in sample_keys:
+                current_col = "messages"
+
+        if expected_col and expected_col != current_col:
+            warnings.append(
+                f"Model expects '{expected_col}' but dataset has '{current_col}' — renaming."
+            )
+            dataset = rename_chat_column_in_list(dataset, current_col, expected_col)
+            current_col = expected_col
+
         # Return as list
         return {
             "dataset": dataset,
             "detected_format": vlm_structure["format"],
             "final_format": "vlm_messages",
-            "chat_column": "messages",
+            "chat_column": current_col,
             "is_vlm": True,
             "is_multimodal": multimodal_info["is_multimodal"],
             "multimodal_info": multimodal_info,

studio/backend/utils/datasets/format_conversion.py

@@ -8,6 +8,43 @@ This module contains functions for converting between dataset formats
 from datasets import IterableDataset
 
 
+def get_expected_chat_column(model):
+    """
+    Inspect the model's forward() signature to determine if it expects
+    'messages' or 'conversations' as a column name.
+
+    Returns:
+        str or None: 'messages', 'conversations', or None if neither found.
+    """
+    import inspect
+    try:
+        sig = inspect.signature(model.forward)
+        params = list(sig.parameters.keys())
+
+        if "messages" in params:
+            return "messages"
+        elif "conversations" in params:
+            return "conversations"
+    except (ValueError, TypeError):
+        pass
+    return None
+
+
+def rename_chat_column_in_list(data, from_col, to_col):
+    """
+    Rename a chat column key in a list of dicts (for VLM data).
+    """
+    if from_col == to_col:
+        return data
+    renamed = []
+    for item in data:
+        new_item = {}
+        for k, v in item.items():
+            new_item[to_col if k == from_col else k] = v
+        renamed.append(new_item)
+    return renamed
+
+
 def standardize_chat_format(
     dataset,
     tokenizer=None,