diff --git a/README.md b/README.md
index d345587..54b2ac4 100644
--- a/README.md
+++ b/README.md
@@ -50,13 +50,15 @@ The model name in string, defaults to None.
 If None, the operator will be initialized without specified model.
 
 Supported model names: NLP transformers models listed in [Huggingface Models](https://huggingface.co/models).
-Please note that only models listed in `supported_model_names` are tested.
-You can refer to [Towhee Pipeline]() for model performance.
+Please note that only models listed in `supported_model_names` are tested by us.
+You can refer to [Towhee Pipeline](https://towhee.io/tasks/detail/pipeline/sentence-similarity) for model performance.
 
 ***checkpoint_path***: *str*
 
 The path to local checkpoint, defaults to None.
-If None, the operator will download and load pretrained model by `model_name` from Huggingface transformers.
+- If None, the operator will download and load pretrained model by `model_name` from Huggingface transformers.
+- The checkpoint path could be a path to a directory containing model weights saved using [`save_pretrained()` by HuggingFace Transformers](https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.save_pretrained).
+- Or you can pass a path to a PyTorch `state_dict` save file.
 
 ***tokenizer***: *object*
 
diff --git a/auto_transformers.py b/auto_transformers.py
index 236a841..580e222 100644
--- a/auto_transformers.py
+++ b/auto_transformers.py
@@ -44,15 +44,27 @@ t_logging.set_verbosity_error()
 
 
 def create_model(model_name, checkpoint_path, device):
-    model = AutoModel.from_pretrained(model_name).to(device)
+    _torch_weights = False
+    if checkpoint_path:
+        if os.path.isdir(checkpoint_path) and \
+            os.path.exists(os.path.join(checkpoint_path, 'config.json')):
+            model = AutoModel.from_pretrained(checkpoint_path)
+        else:
+            model = AutoConfig.from_pretrained(model_name)
+            _torch_weights = True
+    else:
+        model = AutoModel.from_pretrained(model_name)
+        
+    model = model.to(device)
     if hasattr(model, 'pooler') and model.pooler:
         model.pooler = None
-    if checkpoint_path:
+
+    if _torch_weights:
         try:
             state_dict = torch.load(checkpoint_path, map_location=device)
             model.load_state_dict(state_dict)
         except Exception:
-            log.error(f'Fail to load weights from {checkpoint_path}')
+            log.error(f'Failed to load weights from {checkpoint_path}')
     model.eval()
     return model