diff --git a/modelscope/msdatasets/download/dataset_builder.py b/modelscope/msdatasets/download/dataset_builder.py
index 0c5c4154..84563668 100644
--- a/modelscope/msdatasets/download/dataset_builder.py
+++ b/modelscope/msdatasets/download/dataset_builder.py
@@ -330,6 +330,7 @@ class IterableDatasetBuilder(csv.Csv):
 
         super().__init__(
             cache_dir=self.cache_build_dir,
+            dataset_name=self.dataset_name,
             config_name=self.namespace,
             hash=sub_dir_hash,
             data_files=None,  # TODO: self.meta_data_files,
diff --git a/modelscope/msdatasets/ms_dataset.py b/modelscope/msdatasets/ms_dataset.py
index 4b129698..43f5bad1 100644
--- a/modelscope/msdatasets/ms_dataset.py
+++ b/modelscope/msdatasets/ms_dataset.py
@@ -268,17 +268,15 @@ class MsDataset:
             return dataset_inst
         # Load from the huggingface hub
         elif hub == Hubs.huggingface:
-            dataset_inst = RemoteDataLoaderManager(
-                dataset_context_config).load_dataset(
-                    RemoteDataLoaderType.HF_DATA_LOADER)
-            dataset_inst = MsDataset.to_ms_dataset(dataset_inst, target=target)
-            if isinstance(dataset_inst, MsDataset):
-                dataset_inst._dataset_context_config = dataset_context_config
-                if custom_cfg:
-                    dataset_inst.to_custom_dataset(
-                        custom_cfg=custom_cfg, **config_kwargs)
-                    dataset_inst.is_custom = True
-            return dataset_inst
+            from datasets import load_dataset
+            return load_dataset(
+                dataset_name,
+                name=subset_name,
+                split=split,
+                streaming=use_streaming,
+                download_mode=download_mode.value,
+                **config_kwargs)
+
         # Load from the modelscope hub
         elif hub == Hubs.modelscope:
 
diff --git a/tests/msdatasets/test_ms_dataset.py b/tests/msdatasets/test_ms_dataset.py
index 83ffd3f8..dfbdfa8c 100644
--- a/tests/msdatasets/test_ms_dataset.py
+++ b/tests/msdatasets/test_ms_dataset.py
@@ -169,17 +169,6 @@ class MsDatasetTest(unittest.TestCase):
             'speech_asr_aishell1_trainsets', namespace='speech_asr')
         print(next(iter(ms_ds_asr['train'])))
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
-    @require_torch
-    def test_to_torch_dataset_img(self):
-        ms_image_train = MsDataset.load(
-            'fixtures_image_utils', namespace='damotest', split='test')
-        pt_dataset = ms_image_train.to_torch_dataset(
-            preprocessors=ImgPreprocessor(image_path='file'))
-        import torch
-        dataloader = torch.utils.data.DataLoader(pt_dataset, batch_size=5)
-        print(next(iter(dataloader)))
-
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     @require_tf
     def test_to_tf_dataset_img(self):
@@ -229,7 +218,7 @@ class MsDatasetTest(unittest.TestCase):
         print(data_example)
         assert data_example.values()
 
-    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
+    @unittest.skipUnless(test_level() >= 3, 'skip test in current test level')
     def test_streaming_load_img_object(self):
         """Test case for iterating PIL object."""
         from PIL.PngImagePlugin import PngImageFile
@@ -238,7 +227,7 @@ class MsDatasetTest(unittest.TestCase):
             subset_name='default',
             namespace='huizheng',
             split='train',
-            use_streaming=True)
+            use_streaming=False)
         data_example = next(iter(dataset))
         print(data_example)
         assert data_example.values()
@@ -247,7 +236,8 @@ class MsDatasetTest(unittest.TestCase):
     def test_to_ms_dataset(self):
         """Test case for converting huggingface dataset to `MsDataset` instance."""
         from datasets.load import load_dataset
-        hf_dataset = load_dataset('beans', split='train', streaming=True)
+        hf_dataset = load_dataset(
+            'AI-Lab-Makerere/beans', split='train', streaming=True)
         ms_dataset = MsDataset.to_ms_dataset(hf_dataset)
         data_example = next(iter(ms_dataset))
         print(data_example)