add

models/LLaVA/build/lib/llava/train/llava_trainer.py

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+import os
+import torch
+import torch.nn as nn
+
+from transformers import Trainer
+from typing import Dict, Optional, Sequence
+
+
+def unwrap_model(model: nn.Module) -> nn.Module:
+    """
+    Recursively unwraps a model from potential containers (as used in distributed training).
+
+    Args:
+        model (`torch.nn.Module`): The model to unwrap.
+    """
+    # since there could be multiple levels of wrapping, unwrap recursively
+    if hasattr(model, "module"):
+        return unwrap_model(model.module)
+    else:
+        return model
+
+
+class LLaVATrainer(Trainer):
+
+    def _save(self, output_dir: Optional[str] = None, state_dict=None):
+        if getattr(self.args, 'tune_mm_mlp_adapter', False):
+            # Save the model
+            _state_dict = state_dict
+            if _state_dict is None:
+                # Only save the model itself if we are using distributed training
+                model_to_save = unwrap_model(self.model)
+                _state_dict = model_to_save.state_dict()
+
+            weight_to_save = {}
+            keys_to_match = ['mm_projector', 'embed_tokens', 'embed_in']
+            for k, v in _state_dict.items():
+                if any(key_match in k for key_match in keys_to_match):
+                    weight_to_save[k] = v
+
+            current_folder = output_dir.split('/')[-1]
+            parent_folder = os.path.dirname(output_dir)
+            if current_folder.startswith('checkpoint-'):
+                mm_projector_folder = os.path.join(parent_folder, "mm_projector")
+                os.makedirs(mm_projector_folder, exist_ok=True)
+                torch.save(weight_to_save, os.path.join(mm_projector_folder, f'{current_folder}.bin'))
+            else:
+                torch.save(weight_to_save, os.path.join(output_dir, f'mm_projector.bin'))
+
+        super(LLaVATrainer, self)._save(output_dir, state_dict)