diff --git a/swift/llm/dataset/preprocessor/core.py b/swift/llm/dataset/preprocessor/core.py
index 8fdcb9e491..238d999ea7 100644
--- a/swift/llm/dataset/preprocessor/core.py
+++ b/swift/llm/dataset/preprocessor/core.py
@@ -314,7 +314,6 @@ def __call__(
             dataset = sample_dataset(dataset, self.dataset_sample, True, self.random_state)
 
         map_kwargs = {'batched': True, 'batch_size': batch_size}
-        cache_file_name = None
         if isinstance(dataset, HfDataset):
             if not load_from_cache_file and is_dist() and not is_master():
                 load_from_cache_file = True
@@ -326,11 +325,11 @@ def __call__(
         dataset = RowPreprocessor.get_features_dataset(dataset)
         if 'solution' in dataset.features:
             with safe_ddp_context(None, True):
-                if not dataset.cache_files:
-                    cache_file_name = os.path.join(get_cache_dir(), 'datasets', 'map_cache',
-                                                   f'{dataset._fingerprint}.arrow')
-                dataset = dataset.map(
-                    lambda x: {'__#solution': x['solution']}, **map_kwargs, cache_file_name=cache_file_name)
+                if isinstance(dataset, HfDataset) and not dataset.cache_files:
+                    map_kwargs['cache_file_name'] = os.path.join(get_cache_dir(), 'datasets', 'map_cache',
+                                                                 f'{dataset._fingerprint}.arrow')
+                dataset = dataset.map(lambda x: {'__#solution': x['solution']}, **map_kwargs)
+                map_kwargs.pop('cache_file_name', None)
         dataset = self._rename_columns(dataset)
         dataset = self.prepare_dataset(dataset)
         dataset = self._cast_pil_image(dataset)
@@ -338,9 +337,9 @@ def __call__(
         ignore_max_length_error = True if isinstance(dataset, HfDataset) and num_proc > 1 else False
         with self._patch_arrow_writer(), safe_ddp_context(None, True):
             try:
-                if not dataset.cache_files:
-                    cache_file_name = os.path.join(get_cache_dir(), 'datasets', 'map_cache',
-                                                   f'{dataset._fingerprint}.arrow')
+                if isinstance(dataset, HfDataset) and not dataset.cache_files:
+                    map_kwargs['cache_file_name'] = os.path.join(get_cache_dir(), 'datasets', 'map_cache',
+                                                                 f'{dataset._fingerprint}.arrow')
                 dataset_mapped = dataset.map(
                     self.batched_preprocess,
                     fn_kwargs={
@@ -348,7 +347,6 @@ def __call__(
                         'ignore_max_length_error': ignore_max_length_error
                     },
                     remove_columns=list(dataset.features.keys()),
-                    cache_file_name=cache_file_name,
                     **map_kwargs)
             except NotImplementedError:
                 pass