diff --git a/modelscope/models/multi_modal/mplug/modeling_mplug.py b/modelscope/models/multi_modal/mplug/modeling_mplug.py
index ec491f1d..1d003f5c 100755
--- a/modelscope/models/multi_modal/mplug/modeling_mplug.py
+++ b/modelscope/models/multi_modal/mplug/modeling_mplug.py
@@ -1850,7 +1850,7 @@ class MPlug(PreTrainedModel):
             self.config_fusion, add_pooling_layer=False)
 
     @classmethod
-    def from_pretrained(cls, model_dir, load_checkpoint=True):
+    def from_pretrained(cls, model_dir, task=None, load_checkpoint=True):
         from modelscope.utils.constant import Tasks
 
         task_mapping = {
@@ -1861,7 +1861,9 @@ class MPlug(PreTrainedModel):
         config = cls.config_class.from_yaml_file(
             os.path.join(model_dir, CONFIG_NAME))
         config.model_dir = model_dir
-        model = task_mapping[config.task](config)
+        if task is None:
+            task = config.task
+        model = task_mapping[task](config)
         if load_checkpoint:
             checkpoint_path = os.path.join(model_dir,
                                            ModelFile.TORCH_MODEL_BIN_FILE)
diff --git a/modelscope/models/multi_modal/mplug_for_all_tasks.py b/modelscope/models/multi_modal/mplug_for_all_tasks.py
index 7de8d291..4d2a6ac2 100644
--- a/modelscope/models/multi_modal/mplug_for_all_tasks.py
+++ b/modelscope/models/multi_modal/mplug_for_all_tasks.py
@@ -20,7 +20,7 @@ __all__ = ['MPlugForAllTasks']
 @MODELS.register_module(Tasks.image_text_retrieval, module_name=Models.mplug)
 class MPlugForAllTasks(TorchModel):
 
-    def __init__(self, model_dir: str, *args, **kwargs):
+    def __init__(self, model_dir: str, task=None, *args, **kwargs):
         """initialize the mplug model from the `model_dir` path.
         Args:
             model_dir (str): the model path.
@@ -28,7 +28,7 @@ class MPlugForAllTasks(TorchModel):
 
         super().__init__(model_dir, *args, **kwargs)
         from modelscope.models.multi_modal.mplug import MPlug
-        self.model = MPlug.from_pretrained(model_dir)
+        self.model = MPlug.from_pretrained(model_dir, task=task)
         self.tokenizer = self.model.tokenizer
 
     def forward(self, input: Dict[str, Tensor]) -> Dict[str, Tensor]:
diff --git a/modelscope/trainers/multi_modal/mplug/mplug_trainer.py b/modelscope/trainers/multi_modal/mplug/mplug_trainer.py
index def66220..fb456719 100644
--- a/modelscope/trainers/multi_modal/mplug/mplug_trainer.py
+++ b/modelscope/trainers/multi_modal/mplug/mplug_trainer.py
@@ -1,18 +1,29 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 
 from collections.abc import Mapping
+from typing import Optional, Union
 
 import torch
+from torch import nn
 
 from modelscope.metainfo import Trainers
+from modelscope.models import Model, TorchModel
 from modelscope.outputs import OutputKeys
-from modelscope.trainers import NlpEpochBasedTrainer
+from modelscope.trainers import EpochBasedTrainer
 from modelscope.trainers.builder import TRAINERS
 from modelscope.utils.file_utils import func_receive_dict_inputs
 
 
 @TRAINERS.register_module(module_name=Trainers.mplug)
-class MPlugTrainer(NlpEpochBasedTrainer):
+class MPlugTrainer(EpochBasedTrainer):
+
+    def __init__(self, *args, **kwargs):
+        self.task: Optional[str] = kwargs.pop('task', None)
+        super().__init__(*args, **kwargs)
+
+    def build_model(self) -> Union[nn.Module, TorchModel]:
+        return Model.from_pretrained(
+            self.model_dir, task=self.task, cfg_dict=self.cfg)
 
     def _decode(self, tokens):
         tokenizer = self.eval_preprocessor.tokenizer
diff --git a/tests/trainers/test_finetune_mplug.py b/tests/trainers/test_finetune_mplug.py
index 46664114..c64e1285 100644
--- a/tests/trainers/test_finetune_mplug.py
+++ b/tests/trainers/test_finetune_mplug.py
@@ -9,7 +9,7 @@ from modelscope.metainfo import Trainers
 from modelscope.models.multi_modal import MPlugForAllTasks
 from modelscope.msdatasets import MsDataset
 from modelscope.trainers import EpochBasedTrainer, build_trainer
-from modelscope.utils.constant import ModelFile
+from modelscope.utils.constant import ModelFile, Tasks
 from modelscope.utils.test_utils import test_level
 
 
@@ -40,11 +40,12 @@ class TestFinetuneMPlug(unittest.TestCase):
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_trainer_with_caption(self):
         kwargs = dict(
-            model='damo/mplug_image-captioning_coco_base_en',
+            model='damo/mplug_backbone_base_en',
             train_dataset=self.train_dataset,
             eval_dataset=self.test_dataset,
             max_epochs=self.max_epochs,
-            work_dir=self.tmp_dir)
+            work_dir=self.tmp_dir,
+            task=Tasks.image_captioning)
 
         trainer: EpochBasedTrainer = build_trainer(
             name=Trainers.mplug, default_args=kwargs)
@@ -52,9 +53,9 @@ class TestFinetuneMPlug(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_trainer_with_caption_with_model_and_args(self):
-        cache_path = snapshot_download(
-            'damo/mplug_image-captioning_coco_base_en')
-        model = MPlugForAllTasks.from_pretrained(cache_path)
+        cache_path = snapshot_download('damo/mplug_backbone_base_en')
+        model = MPlugForAllTasks.from_pretrained(
+            cache_path, task=Tasks.image_captioning)
         kwargs = dict(
             cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
             model=model,
@@ -74,11 +75,12 @@ class TestFinetuneMPlug(unittest.TestCase):
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_trainer_with_vqa(self):
         kwargs = dict(
-            model='damo/mplug_visual-question-answering_coco_large_en',
+            model='damo/mplug_backbone_base_en',
             train_dataset=self.train_dataset,
             eval_dataset=self.test_dataset,
             max_epochs=self.max_epochs,
-            work_dir=self.tmp_dir)
+            work_dir=self.tmp_dir,
+            task=Tasks.visual_question_answering)
 
         trainer: EpochBasedTrainer = build_trainer(
             name=Trainers.mplug, default_args=kwargs)
@@ -88,7 +90,8 @@ class TestFinetuneMPlug(unittest.TestCase):
     def test_trainer_with_vqa_with_model_and_args(self):
         cache_path = snapshot_download(
             'damo/mplug_visual-question-answering_coco_large_en')
-        model = MPlugForAllTasks.from_pretrained(cache_path)
+        model = MPlugForAllTasks.from_pretrained(
+            cache_path, task=Tasks.visual_question_answering)
         kwargs = dict(
             cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
             model=model,
@@ -108,11 +111,12 @@ class TestFinetuneMPlug(unittest.TestCase):
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_trainer_with_retrieval(self):
         kwargs = dict(
-            model='damo/mplug_image-text-retrieval_flickr30k_large_en',
+            model='damo/mplug_backbone_base_en',
             train_dataset=self.train_dataset,
             eval_dataset=self.test_dataset,
             max_epochs=self.max_epochs,
-            work_dir=self.tmp_dir)
+            work_dir=self.tmp_dir,
+            task=Tasks.image_text_retrieval)
 
         trainer: EpochBasedTrainer = build_trainer(
             name=Trainers.mplug, default_args=kwargs)
@@ -120,9 +124,9 @@ class TestFinetuneMPlug(unittest.TestCase):
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_trainer_with_retrieval_with_model_and_args(self):
-        cache_path = snapshot_download(
-            'damo/mplug_image-text-retrieval_flickr30k_large_en')
-        model = MPlugForAllTasks.from_pretrained(cache_path)
+        cache_path = snapshot_download('damo/mplug_backbone_base_en')
+        model = MPlugForAllTasks.from_pretrained(
+            cache_path, task=Tasks.image_text_retrieval)
         kwargs = dict(
             cfg_file=os.path.join(cache_path, ModelFile.CONFIGURATION),
             model=model,