diff --git a/auto_transformers.py b/auto_transformers.py
index 9f22bce..225b3e2 100644
--- a/auto_transformers.py
+++ b/auto_transformers.py
@@ -22,6 +22,7 @@ from transformers import AutoTokenizer, AutoModel, AutoModelForMaskedLM, AutoMod
 
 from towhee.operator import NNOperator
 from towhee import register
+from towhee.dc2 import accelerate
 
 import warnings
 import logging
@@ -34,6 +35,26 @@ log = logging.getLogger('run_op')
 warnings.filterwarnings('ignore')
 
 
+@accelerate
+class Model:
+    def __init__(self, model_name, device, checkpoint_path):
+        try:
+            self.model = AutoModel.from_pretrained(model_name).to(device)
+        except Exception as e:
+            log.error(f"Fail to load model by name: {self.model_name}")
+            raise e
+        if checkpoint_path:
+            try:
+                state_dict = torch.load(checkpoint_path, map_location=device)
+                self.model.load_state_dict(state_dict)
+            except Exception as e:
+                log.error(f"Fail to load state dict from {checkpoint_path}: {e}")
+        self.model.eval()
+
+    def __call__(self, *args, **kwargs):
+        return self.model(*args, **kwargs)
+
+
 @register(output_schema=['vec'])
 class AutoTransformers(NNOperator):
     """
@@ -57,26 +78,14 @@ class AutoTransformers(NNOperator):
         if device is None:
             device = 'cuda' if torch.cuda.is_available() else 'cpu'
         self.device = device
+
+        model_list = self.supported_model_names()
+        assert model_name in model_list, f"Invalid model name: {model_name}. Supported model names: {model_list}"
         self.model_name = model_name
 
         if self.model_name:
-            model_list = self.supported_model_names()
-            assert model_name in model_list, f"Invalid model name: {model_name}. Supported model names: {model_list}"
-
-            try:
-                self.model = AutoModel.from_pretrained(model_name).to(self.device)
-                self.configs = self.model.config
-            except Exception as e:
-                log.error(f"Fail to load model by name: {self.model_name}")
-                raise e
-            if checkpoint_path:
-                try:
-                    state_dict = torch.load(checkpoint_path, map_location=self.device)
-                    self.model.load_state_dict(state_dict)
-                except Exception as e:
-                    log.error(f"Fail to load state dict from {checkpoint_path}: {e}")
-            self.model.eval()
-
+            self.model = Model(model_name=self.model_name, device=self.device, checkpoint_path=checkpoint_path)
+            self.configs = self.model.model.config
             if tokenizer is None:
                 try:
                     self.tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -135,13 +144,13 @@ class AutoTransformers(NNOperator):
             from transformers.onnx.features import FeaturesManager
             from transformers.onnx import export
             model_kind, model_onnx_config = FeaturesManager.check_supported_model_or_raise(
-                self.model, feature='default')
-            onnx_config = model_onnx_config(self.model.config)
+                self.model.model, feature='default')
+            onnx_config = model_onnx_config(self.configs)
             if os.path.isdir(path):
                 shutil.rmtree(path)
             onnx_inputs, onnx_outputs = export(
                 self.tokenizer,
-                self.model,
+                self.model.model,
                 config=onnx_config,
                 opset=13,
                 output=Path(path+'.onnx')