Upload ChessBot Chess model

modeling_chessbot.py

@@ -527,77 +527,6 @@ class ChessBotModel(ChessBotPreTrainedModel):
         # Initialize weights
         self.post_init()
     
-    @classmethod
-    def from_pretrained(cls, model_path, **kwargs):
-        """
-        Load a pretrained model from a directory (HuggingFace compatible)
-        """
-        import os
-        
-        # Load config
-        config_path = os.path.join(model_path, "config.json")
-        if os.path.exists(config_path):
-            config = ChessBotConfig.from_pretrained(model_path)
-        else:
-            config = ChessBotConfig()
-        
-        # Create model instance
-        model = cls(config)
-        
-        # Load weights
-        model_file = None
-        for filename in ["pytorch_model.bin", "model.safetensors"]:
-            full_path = os.path.join(model_path, filename)
-            if os.path.exists(full_path):
-                model_file = full_path
-                break
-        
-        if model_file is None:
-            raise FileNotFoundError(f"No model file found in {model_path}")
-        
-        if model_file.endswith('.safetensors'):
-            # Handle safetensors format
-            try:
-                from safetensors import safe_open
-                state_dict = {}
-                with safe_open(model_file, framework="pt", device="cpu") as f:
-                    for key in f.keys():
-                        state_dict[key] = f.get_tensor(key)
-            except ImportError:
-                raise ImportError("safetensors library is required to load .safetensors files. Install with: pip install safetensors")
-        else:
-            # Handle pytorch format
-            state_dict = torch.load(model_file, map_location="cpu")
-        
-        # Load state dict into model
-        model.load_state_dict(state_dict, strict=False)
-        
-        return model
-    
-    def save_pretrained(self, save_directory, safe_serialization=False):
-        """
-        Save the model to a directory (HuggingFace compatible)
-        """
-        import os
-        os.makedirs(save_directory, exist_ok=True)
-        
-        # Save config
-        self.config.save_pretrained(save_directory)
-        
-        # Save model weights
-        if safe_serialization:
-            try:
-                from safetensors.torch import save_file
-                model_path = os.path.join(save_directory, "model.safetensors")
-                save_file(self.state_dict(), model_path)
-            except ImportError:
-                print("⚠ Warning: safetensors not available, falling back to pytorch_model.bin")
-                model_path = os.path.join(save_directory, "pytorch_model.bin")
-                torch.save(self.state_dict(), model_path)
-        else:
-            model_path = os.path.join(save_directory, "pytorch_model.bin")
-            torch.save(self.state_dict(), model_path)
-    
     def forward(self, input_ids, attention_mask=None, compute_loss=False):
         """
         Forward pass compatible with both HuggingFace interface and original interface