Update modeling.py

modeling.py

@@ -268,53 +268,6 @@ class GSLMForCausalLM(GSLMPreTrainedModel):
         # Initialize weights
         self.post_init()
         
-    @classmethod
-    def from_pretrained(cls, pretrained_model_name_or_path, *model_args,
-                        **kwargs):
-        """
-        Load **either** a HF‑native checkpoint **or** a fairseq‑style
-        checkpoint transparently.
-        """
-        trust_remote_code = kwargs.pop("trust_remote_code", True)
-
-        # try:
-        #     # First try the normal Hugging‑Face path
-        #     return super().from_pretrained(
-        #         pretrained_model_name_or_path,
-        #         *model_args,
-        #         trust_remote_code=trust_remote_code,
-        #         **kwargs,
-        #     )
-
-        # except (RuntimeError, KeyError) as err:
-
-        # 1. Load *raw* tensors (no strict shape checks yet)
-        import torch
-        state_dict = torch.load(
-            pretrained_model_name_or_path,
-            map_location="cpu",
-            weights_only=False,
-        )["model"]
-
-        # 2. Convert key names
-        converted = _fairseq_to_hf(state_dict)
-
-        # 3. Build an *uninitialised* model then load weights
-        config = kwargs.pop("config", None)  # a config object or None
-        with no_init_weights():
-            model = cls(config) if config is not None else cls.from_config(
-                pretrained_model_name_or_path
-            )
-
-        missing, unexpected = model.load_state_dict(converted, strict=False)
-        if missing or unexpected:
-            print(
-                f"Checkpoint conversion finished with "
-                f"{len(missing)} missing and {len(unexpected)} "
-                "unexpected keys."
-            )
-        return model
-        
     def get_input_embeddings(self):
         return self.transformer.wte