diff --git a/modelscope/outputs/outputs.py b/modelscope/outputs/outputs.py
index 82c5ce10..368abad6 100644
--- a/modelscope/outputs/outputs.py
+++ b/modelscope/outputs/outputs.py
@@ -562,6 +562,7 @@ TASK_OUTPUTS = {
     #   }
     Tasks.facial_expression_recognition:
     [OutputKeys.SCORES, OutputKeys.LABELS],
+    Tasks.general_recognition: [OutputKeys.SCORES, OutputKeys.LABELS],
 
     # face processing base result for single img
     #   {
diff --git a/modelscope/pipeline_inputs.py b/modelscope/pipeline_inputs.py
index f465a722..bffbebbd 100644
--- a/modelscope/pipeline_inputs.py
+++ b/modelscope/pipeline_inputs.py
@@ -82,14 +82,28 @@ def check_input_type(input_type, input):
 
 
 TASK_INPUTS = {
+
+    # if task input is single var, value is  InputType
+    # if task input is a tuple,  value is tuple of InputType
+    # if task input is a dict, value is a dict of InputType, where key
+    # equals the one needed in pipeline input dict
+    # if task input is a list, value is a set of input format, in which
+    # each element corresponds to one input format as described above and
+    # must include a dict format.
+
+
+    Tasks.task_template: {
+        'image': InputType.IMAGE,
+        'text': InputType.TEXT
+    },
+    # ============ vision tasks ===================
+
     Tasks.image_text_retrieval: {
         InputKeys.IMAGE: InputType.IMAGE,
         InputKeys.TEXT: InputType.TEXT
     },
-    Tasks.general_recognition: {
-        InputKeys.IMAGE: InputType.IMAGE,
-        InputKeys.TEXT: InputType.TEXT
-    },
+    Tasks.general_recognition:
+    InputType.IMAGE,
     Tasks.video_depth_estimation: {
         InputKeys.IMAGE: InputType.IMAGE,
         InputKeys.TEXT: InputType.TEXT
@@ -110,20 +124,6 @@ TASK_INPUTS = {
     InputType.VIDEO,
 
 
-
-    Tasks.task_template: {
-        'image': InputType.IMAGE,
-        'text': InputType.TEXT
-    },
-    # if task input is single var, value is  InputType
-    # if task input is a tuple,  value is tuple of InputType
-    # if task input is a dict, value is a dict of InputType, where key
-    # equals the one needed in pipeline input dict
-    # if task input is a list, value is a set of input format, in which
-    # each element corresponds to one input format as described above and
-    # must include a dict format.
-
-    # ============ vision tasks ===================
     Tasks.ocr_detection:
     InputType.IMAGE,
     Tasks.ocr_recognition:
diff --git a/modelscope/utils/hf_util.py b/modelscope/utils/hf_util.py
index 3abcce6d..6ef98ccf 100644
--- a/modelscope/utils/hf_util.py
+++ b/modelscope/utils/hf_util.py
@@ -91,12 +91,13 @@ def check_hf_code(model_dir: str, auto_class: type,
         raise FileNotFoundError(f'{config_path} is not found')
     config_dict = PretrainedConfig.get_config_dict(config_path)[0]
     auto_class_name = auto_class.__name__
+    if auto_class is AutoTokenizerHF:
+        tokenizer_config = get_tokenizer_config(model_dir)
     # load from repo
     if trust_remote_code:
         has_remote_code = False
         if auto_class is AutoTokenizerHF:
-            tokenizer_config_dict = get_tokenizer_config(model_dir)
-            auto_map = tokenizer_config_dict.get('auto_map', None)
+            auto_map = tokenizer_config.get('auto_map', None)
             if auto_map is not None:
                 module_name = auto_map.get(auto_class_name, None)
                 if module_name is not None:
@@ -129,6 +130,9 @@ def check_hf_code(model_dir: str, auto_class: type,
                 f'{model_type} not found in HF `CONFIG_MAPPING`{trust_remote_code_info}'
             )
     elif auto_class is AutoTokenizerHF:
+        tokenizer_class = tokenizer_config.get('tokenizer_class')
+        if tokenizer_class is not None:
+            return
         if model_type not in TOKENIZER_MAPPING_NAMES:
             raise ValueError(
                 f'{model_type} not found in HF `TOKENIZER_MAPPING_NAMES`{trust_remote_code_info}'
diff --git a/modelscope/utils/input_output.py b/modelscope/utils/input_output.py
index dbe5861d..d8e32cce 100644
--- a/modelscope/utils/input_output.py
+++ b/modelscope/utils/input_output.py
@@ -656,7 +656,7 @@ def service_base64_input_to_pipeline_input(task_name, body):
 
     if isinstance(service_input, (str, int, float)):
         return service_input, parameters
-    task_input_info = TASK_INPUTS[task_name]
+    task_input_info = TASK_INPUTS.get(task_name, None)
     if isinstance(task_input_info, str):  # no input key default
         if isinstance(service_input, dict):
             return base64_decoder_map[task_input_info](list(
@@ -767,9 +767,7 @@ def pipeline_output_to_service_base64_output(task_name, pipeline_output):
         pipeline_output (object): The pipeline output.
     """
     json_serializable_output = {}
-    task_outputs = []
-    if task_name in TASK_OUTPUTS:
-        task_outputs = TASK_OUTPUTS[task_name]
+    task_outputs = TASK_OUTPUTS.get(task_name, [])
     # TODO: for batch
     if isinstance(pipeline_output, list):
         pipeline_output = pipeline_output[0]