Handling an exception related to HQQ quantization in modeling (#36702)

* adding exception

* style

* add types

src/transformers/modeling_utils.py

@@ -709,9 +709,19 @@ def _find_identical(tensors: List[Set[str]], state_dict: Dict[str, torch.Tensor]
 
 
 def _infer_parameter_dtype(
-    model: "PreTrainedModel", param_name: str, empty_param, keep_in_fp32_modules=None
+    model: "PreTrainedModel",
+    param_name: str,
+    empty_param: torch.Tensor,
+    keep_in_fp32_modules: Optional[List[str]] = None,
+    hf_quantizer: Optional[HfQuantizer] = None,
 ) -> Union[bool, Optional[torch.dtype]]:
-    old_param = model.get_parameter_or_buffer(param_name)
+    try:
+        old_param = model.get_parameter_or_buffer(param_name)
+    except Exception as e:
+        if hf_quantizer is not None and hf_quantizer.quantization_config.quant_method == QuantizationMethod.HQQ:
+            return True, None
+        else:
+            raise e
     is_torch_e4m3fn_available = hasattr(torch, "float8_e4m3fn")
     # We convert floating dtypes to the `dtype` passed except for float8_e4m3fn type. We also want to keep the buffers/params
     # in int/uint/bool and not cast them.
@@ -781,6 +791,7 @@ def _load_state_dict_into_meta_model(
             param_name,
             empty_param,
             keep_in_fp32_modules,
+            hf_quantizer,
         )
 
         if device_mesh is not None:  # In this case, the param is already on the correct device!