diff --git a/neural_compressor/torch/algorithms/layer_wise/utils.py b/neural_compressor/torch/algorithms/layer_wise/utils.py
index bbe59de3fe4..0cbe23d5cca 100644
--- a/neural_compressor/torch/algorithms/layer_wise/utils.py
+++ b/neural_compressor/torch/algorithms/layer_wise/utils.py
@@ -90,7 +90,7 @@ def get_named_children(model, pre=[]):
     return module_list
 
 
-def dowload_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):
+def dowload_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):  # pragma: no cover
     """Download hugging face model from hf hub."""
     from huggingface_hub.constants import DEFAULT_REVISION, HUGGINGFACE_HUB_CACHE
     from huggingface_hub.file_download import REGEX_COMMIT_HASH, repo_folder_name
@@ -122,7 +122,7 @@ def dowload_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):
         return file_path
 
 
-def load_empty_model(pretrained_model_name_or_path, cls=AutoModelForCausalLM, **kwargs):
+def load_empty_model(pretrained_model_name_or_path, cls=AutoModelForCausalLM, **kwargs):  # pragma: no cover
     """Load a empty model."""
     is_local = os.path.isdir(pretrained_model_name_or_path)
     if is_local:  # pragma: no cover
diff --git a/neural_compressor/torch/utils/__init__.py b/neural_compressor/torch/utils/__init__.py
index 25aadaa6d66..dab02a017c6 100644
--- a/neural_compressor/torch/utils/__init__.py
+++ b/neural_compressor/torch/utils/__init__.py
@@ -15,4 +15,3 @@
 from .environ import *
 from .constants import *
 from .utility import *
-from neural_compressor.torch.algorithms.layer_wise import load_empty_model
diff --git a/neural_compressor/torch/utils/utility.py b/neural_compressor/torch/utils/utility.py
index ecdbf2d6243..b210288c1e7 100644
--- a/neural_compressor/torch/utils/utility.py
+++ b/neural_compressor/torch/utils/utility.py
@@ -278,3 +278,65 @@ def get_processor_type_from_user_config(user_processor_type: Optional[Union[str,
     else:
         raise NotImplementedError(f"Unsupported processor type: {user_processor_type}")
     return processor_type
+
+
+def dowload_hf_model(repo_id, cache_dir=None, repo_type=None, revision=None):
+    """Download hugging face model from hf hub."""
+    import os
+
+    from huggingface_hub.constants import DEFAULT_REVISION, HUGGINGFACE_HUB_CACHE
+    from huggingface_hub.file_download import REGEX_COMMIT_HASH, repo_folder_name
+    from huggingface_hub.utils import EntryNotFoundError
+
+    if cache_dir is None:
+        cache_dir = HUGGINGFACE_HUB_CACHE
+    if revision is None:
+        revision = DEFAULT_REVISION
+    if repo_type is None:
+        repo_type = "model"
+    storage_folder = os.path.join(cache_dir, repo_folder_name(repo_id=repo_id, repo_type=repo_type))
+    commit_hash = None
+    if REGEX_COMMIT_HASH.match(revision):
+        commit_hash = revision
+    else:
+        ref_path = os.path.join(storage_folder, "refs", revision)
+        if os.path.exists(ref_path):
+            with open(ref_path) as f:
+                commit_hash = f.read()
+    if storage_folder and commit_hash:
+        pointer_path = os.path.join(storage_folder, "snapshots", commit_hash)
+        if os.path.isdir(pointer_path):
+            return pointer_path
+    else:  # pragma: no cover
+        from huggingface_hub import snapshot_download
+
+        file_path = snapshot_download(repo_id)
+        return file_path
+
+
+def load_empty_model(pretrained_model_name_or_path, cls=None, **kwargs):
+    """Load a empty model."""
+    import os
+
+    from accelerate import init_empty_weights
+    from transformers import AutoConfig, AutoModelForCausalLM
+    from transformers.models.auto.auto_factory import _BaseAutoModelClass
+
+    cls = AutoModelForCausalLM if cls is None else cls
+    is_local = os.path.isdir(pretrained_model_name_or_path)
+    if is_local:  # pragma: no cover
+        path = pretrained_model_name_or_path
+    else:
+        path = dowload_hf_model(pretrained_model_name_or_path)
+    if cls.__base__ == _BaseAutoModelClass:
+        config = AutoConfig.from_pretrained(path, **kwargs)
+        with init_empty_weights():
+            model = cls.from_config(config)
+    else:  # pragma: no cover
+        config = cls.config_class.from_pretrained(path, **kwargs)
+        with init_empty_weights():
+            model = cls(config)
+    model.tie_weights()
+    model.eval()
+    model.path = pretrained_model_name_or_path
+    return model