Update modeling.py

modeling.py

@@ -110,16 +110,16 @@ class MARModel(PreTrainedModel):
         # call the sample_tokens method from the MAR class
         return self.model.sample_tokens(bsz, num_iter, cfg, cfg_schedule, labels, temperature, progress)
 
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        config = MARConfig.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        model = cls(config)
-        safetensors_path = os.path.join(pretrained_model_name_or_path, "checkpoint-last.safetensors")
-        if not os.path.exists(safetensors_path):
-            raise FileNotFoundError(f"safetensors file not found at {safetensors_path}")
-        state_dict = torch.load(safetensors_path, map_location='cpu')
-        model.model.load_state_dict(state_dict)
-        return model
+    # @classmethod
+    # def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+    #     config = MARConfig.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+    #     model = cls(config)
+    #     safetensors_path = os.path.join(pretrained_model_name_or_path, "checkpoint-last.safetensors")
+    #     if not os.path.exists(safetensors_path):
+    #         raise FileNotFoundError(f"safetensors file not found at {safetensors_path}")
+    #     state_dict = torch.load(safetensors_path, map_location='cpu')
+    #     model.model.load_state_dict(state_dict)
+    #     return model
 
 
     def save_pretrained(self, save_directory):