diff --git a/neural_compressor/utils/load_huggingface.py b/neural_compressor/utils/load_huggingface.py
index 6671640722d..c1536736c01 100644
--- a/neural_compressor/utils/load_huggingface.py
+++ b/neural_compressor/utils/load_huggingface.py
@@ -83,7 +83,7 @@ def from_pretrained(cls, model_name_or_path: str, **kwargs) -> torch.nn.Module:
                 **kwargs,
             )
 
-        model_class = eval(f"transformers.{config.architectures[0]}")
+        model_class = transformers.AutoModelForSequenceClassification
         if config.torch_dtype is not torch.int8:
             model = model_class.from_pretrained(
                 model_name_or_path,
@@ -96,81 +96,8 @@ def from_pretrained(cls, model_name_or_path: str, **kwargs) -> torch.nn.Module:
             )
             return model
         else:
-            logger.info("the quantization optimized model is loading.")
-            keys_to_ignore_on_load_unexpected = copy.deepcopy(
-                getattr(model_class, "_keys_to_ignore_on_load_unexpected", None)
-            )
-            keys_to_ignore_on_load_missing = copy.deepcopy(
-                getattr(model_class, "_keys_to_ignore_on_load_missing", None)
-            )
-
-            # Avoid unnecessary warnings resulting from quantized model initialization
-            quantized_keys_to_ignore_on_load = [
-                r"zero_point",
-                r"scale",
-                r"packed_params",
-                r"constant",
-                r"module",
-                r"best_configure",
-            ]
-            if keys_to_ignore_on_load_unexpected is None:
-                model_class._keys_to_ignore_on_load_unexpected = quantized_keys_to_ignore_on_load
-            else:
-                model_class._keys_to_ignore_on_load_unexpected.extend(quantized_keys_to_ignore_on_load)
-            missing_keys_to_ignore_on_load = [r"weight", r"bias"]
-            if keys_to_ignore_on_load_missing is None:
-                model_class._keys_to_ignore_on_load_missing = missing_keys_to_ignore_on_load
-            else:  # pragma: no cover
-                model_class._keys_to_ignore_on_load_missing.extend(missing_keys_to_ignore_on_load)
-
-            if not os.path.isdir(model_name_or_path) and not os.path.isfile(model_name_or_path):  # pragma: no cover
-                from transformers.utils import cached_file
-
-                try:
-                    # Load from URL or cache if already cached
-                    resolved_weights_file = cached_file(
-                        model_name_or_path,
-                        filename=WEIGHTS_NAME,
-                        cache_dir=cache_dir,
-                        force_download=force_download,
-                        resume_download=resume_download,
-                        use_auth_token=use_auth_token,
-                    )
-                except EnvironmentError as err:  # pragma: no cover
-                    logger.error(err)
-                    msg = (
-                        f"Can't load weights for '{model_name_or_path}'. Make sure that:\n\n"
-                        f"- '{model_name_or_path}' is a correct model identifier "
-                        f"listed on 'https://huggingface.co/models'\n  (make sure "
-                        f"'{model_name_or_path}' is not a path to a local directory with "
-                        f"something else, in that case)\n\n- or '{model_name_or_path}' is "
-                        f"the correct path to a directory containing a file "
-                        f"named one of {WEIGHTS_NAME}\n\n"
-                    )
-                    if revision is not None:
-                        msg += (
-                            f"- or '{revision}' is a valid git identifier "
-                            f"(branch name, a tag name, or a commit id) that "
-                            f"exists for this model name as listed on its model "
-                            f"page on 'https://huggingface.co/models'\n\n"
-                        )
-                    raise EnvironmentError(msg)
-            else:
-                resolved_weights_file = os.path.join(model_name_or_path, WEIGHTS_NAME)
-            state_dict = torch.load(resolved_weights_file, {})
-            model = model_class.from_pretrained(
-                model_name_or_path,
-                cache_dir=cache_dir,
-                force_download=force_download,
-                resume_download=resume_download,
-                use_auth_token=use_auth_token,
-                revision=revision,
-                state_dict=state_dict,
-                **kwargs,
-            )
-
-            model_class._keys_to_ignore_on_load_unexpected = keys_to_ignore_on_load_unexpected
-            model_class._keys_to_ignore_on_load_missing = keys_to_ignore_on_load_missing
+            config.torch_dtype = torch.float32
+            model = model_class.from_config(config)
 
             if not os.path.isdir(model_name_or_path) and not os.path.isfile(model_name_or_path):  # pragma: no cover
                 # pylint: disable=E0611