diff --git a/mmengine/model/base_model/base_model.py b/mmengine/model/base_model/base_model.py
index ede27b7a745a09ed816263b6c0863cfa37368f9a..1155d999b532fcecf35c3af99ebc1a375eadb439 100644
--- a/mmengine/model/base_model/base_model.py
+++ b/mmengine/model/base_model/base_model.py
@@ -57,21 +57,21 @@ class BaseModel(BaseModule):
         >>>             return dict(loss=loss)
 
     Args:
-        init_cfg (dict, optional): The weight initialized config for
-            :class:`BaseModule`.
         data_preprocessor (dict, optional): The pre-process config of
             :class:`BaseDataPreprocessor`.
+        init_cfg (dict, optional): The weight initialized config for
+            :class:`BaseModule`.
 
     Attributes:
-        init_cfg (dict, optional): Initialization config dict.
         data_preprocessor (:obj:`BaseDataPreprocessor`): Used for
             pre-processing data sampled by dataloader to the format accepted by
             :meth:`forward`.
+        init_cfg (dict, optional): Initialization config dict.
     """
 
     def __init__(self,
-                 init_cfg: Optional[dict] = None,
-                 data_preprocessor: Optional[Union[dict, nn.Module]] = None):
+                 data_preprocessor: Optional[Union[dict, nn.Module]] = None,
+                 init_cfg: Optional[dict] = None):
         super().__init__(init_cfg)
         if data_preprocessor is None:
             data_preprocessor = dict(type='BaseDataPreprocessor')
diff --git a/tests/test_model/test_base_model/test_base_model.py b/tests/test_model/test_base_model/test_base_model.py
index 280fcead669cd8ead5361d2eff486e0aa89c6a67..222e703d53128af4d7172a1cc82fc65868f6c050 100644
--- a/tests/test_model/test_base_model/test_base_model.py
+++ b/tests/test_model/test_base_model/test_base_model.py
@@ -25,7 +25,7 @@ class CustomDataPreprocessor(BaseDataPreprocessor):
 class ToyModel(BaseModel):
 
     def __init__(self, data_preprocessor=None):
-        super().__init__(None, data_preprocessor=data_preprocessor)
+        super().__init__(data_preprocessor=data_preprocessor, init_cfg=None)
         self.conv = nn.Conv2d(3, 1, 1)
 
     def forward(self, batch_inputs, data_samples=None, mode='tensor'):