diff --git a/examples/pytorch/token_classification/finetune_token_classification.py b/examples/pytorch/token_classification/finetune_token_classification.py
index e3a4b633..cf51ed22 100644
--- a/examples/pytorch/token_classification/finetune_token_classification.py
+++ b/examples/pytorch/token_classification/finetune_token_classification.py
@@ -48,7 +48,7 @@ class TokenClassificationArguments(TrainingArgs):
                 {'dataset.train.labels': label_enumerate_values})
         if config.train.lr_scheduler.type == 'LinearLR':
             config.train.lr_scheduler['total_iters'] = \
-                int(len(self.train_dataset) / self.per_device_train_batch_size) * self.max_epochs
+                int(len(train_dataset) / self.per_device_train_batch_size) * self.max_epochs
         return config
 
     # TODO: Future performance optimization in MsDataset
diff --git a/examples/pytorch/token_classification/run_train.sh b/examples/pytorch/token_classification/run_train_mgeo.sh
similarity index 100%
rename from examples/pytorch/token_classification/run_train.sh
rename to examples/pytorch/token_classification/run_train_mgeo.sh
diff --git a/examples/pytorch/token_classification/run_train_structbert.sh b/examples/pytorch/token_classification/run_train_structbert.sh
new file mode 100644
index 00000000..28967f60
--- /dev/null
+++ b/examples/pytorch/token_classification/run_train_structbert.sh
@@ -0,0 +1,16 @@
+PYTHONPATH=. torchrun examples/pytorch/token_classification/finetune_token_classification.py \
+    --trainer 'nlp-base-trainer' \
+    --work_dir './tmp' \
+    --model 'damo/nlp_structbert_backbone_base_std' \
+    --dataset_name 'GeoGLUE' \
+    --subset_name 'GeoETA' \
+    --train_dataset_params 'first_sequence=tokens,label=ner_tags,sequence_length=128' \
+    --preprocessor 'token-cls-tokenizer' \
+    --preprocessor_padding 'max_length' \
+    --max_epochs 2 \
+    --per_device_train_batch_size 32 \
+    --lr 3e-5 \
+    --save_ckpt_strategy 'by_epoch' \
+    --logging_interval 1 \
+    --eval_strategy 'by_step' \
+    --eval_interval 20 \
diff --git a/tests/trainers/test_gpt3_1_layer_base_qa_cw_tp_1_2.py b/tests/trainers/test_gpt3_1_layer_base_qa_cw_tp_1_2.py
new file mode 100644
index 00000000..1539c4df
--- /dev/null
+++ b/tests/trainers/test_gpt3_1_layer_base_qa_cw_tp_1_2.py
@@ -0,0 +1,413 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import shutil
+import tempfile
+import unittest
+
+import torch
+
+from modelscope.metainfo import Trainers
+from modelscope.msdatasets import MsDataset
+from modelscope.pipelines import pipeline
+from modelscope.trainers import build_trainer
+from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import DistributedTestCase, test_level
+
+
+@unittest.skipIf(not torch.cuda.is_available()
+                 or torch.cuda.device_count() <= 1, 'distributed unittest')
+class TestGPT3OneLayerBaseQAandCWTP(DistributedTestCase):
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+
+        self.tmp_dir = tempfile.TemporaryDirectory().name
+        if not os.path.exists(self.tmp_dir):
+            os.makedirs(self.tmp_dir)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmp_dir)
+        super().tearDown()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_finetune_poetry_tp_1(self):
+        self.start(
+            finetune_poetry_tp_1,
+            num_gpus=gpt3_one_layer_cw_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_1.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_evaluate_poetry_tp_1(self):
+        self.start(
+            evaluate_poetry_tp_1,
+            num_gpus=gpt3_one_layer_cw_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_1.dist_start_cmd)
+
+    # TODO: add gpt3 trainer predict unittest
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_predict_poetry_tp_1(self):
+        self.start(
+            predict_poetry_tp_1,
+            num_gpus=gpt3_one_layer_cw_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_1.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_output_pipeline_poetry_tp_1(self):
+        self.start(
+            pipeline_poetry_tp_1,
+            num_gpus=gpt3_one_layer_cw_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_1.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_finetune_dureader_tp_1(self):
+        self.start(
+            finetune_dureader_tp_1,
+            num_gpus=gpt3_one_layer_qa_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_1.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_evaluate_dureader_tp_1(self):
+        self.start(
+            evaluate_dureader_tp_1,
+            num_gpus=gpt3_one_layer_qa_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_1.dist_start_cmd)
+
+    # TODO: add gpt3 trainer predict unittest
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_predict_dureader_tp_1(self):
+        self.start(
+            predict_dureader_tp_1,
+            num_gpus=gpt3_one_layer_qa_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_1.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_output_pipeline_dureader_tp_1(self):
+        self.start(
+            pipeline_dureader_tp_1,
+            num_gpus=gpt3_one_layer_qa_tp_1.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_1.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_finetune_poetry_tp_2(self):
+        self.start(
+            finetune_poetry_tp_2,
+            num_gpus=gpt3_one_layer_cw_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_2.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_evaluate_poetry_tp_2(self):
+        self.start(
+            evaluate_poetry_tp_2,
+            num_gpus=gpt3_one_layer_cw_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_2.dist_start_cmd)
+
+    # TODO: add gpt3 trainer predict unittest
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_predict_poetry_tp_2(self):
+        self.start(
+            predict_poetry_tp_2,
+            num_gpus=gpt3_one_layer_cw_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_2.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_output_pipeline_poetry_tp_2(self):
+        self.start(
+            pipeline_poetry_tp_2,
+            num_gpus=gpt3_one_layer_cw_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_cw_tp_2.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_finetune_dureader_tp_2(self):
+        self.start(
+            finetune_dureader_tp_2,
+            num_gpus=gpt3_one_layer_qa_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_2.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_evaluate_dureader_tp_2(self):
+        self.start(
+            evaluate_dureader_tp_2,
+            num_gpus=gpt3_one_layer_qa_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_2.dist_start_cmd)
+
+    # TODO: add gpt3 trainer predict unittest
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_predict_dureader_tp_2(self):
+        self.start(
+            predict_dureader_tp_2,
+            num_gpus=gpt3_one_layer_qa_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_2.dist_start_cmd)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_1_layer_output_pipeline_dureader_tp_2(self):
+        self.start(
+            pipeline_dureader_tp_2,
+            num_gpus=gpt3_one_layer_qa_tp_2.tp,
+            dist_start_cmd=gpt3_one_layer_qa_tp_2.dist_start_cmd)
+
+
+class GPT3OneLayerQAandCW():
+
+    def __init__(self, dataset_name, tp, work_dir):
+        self.dataset_name = dataset_name
+        self.tp = tp
+        self.dist_start_cmd = f'torchrun --nproc_per_node {self.tp}'
+        dataset_dict = MsDataset.load(dataset_name)
+        if dataset_name == 'DuReader_robust-QG':
+            self.train_dataset = dataset_dict['train'].remap_columns({'text1': 'src_txt', 'text2': 'tgt_txt'}) \
+                .map(lambda example: {'src_txt': example['src_txt'].replace('[SEP]', '<sep>') + '\n'}).select(range(20))
+            self.eval_dataset = dataset_dict['validation'].remap_columns({'text1': 'src_txt', 'text2': 'tgt_txt'}) \
+                .map(lambda example: {'src_txt': example['src_txt'].replace('[SEP]', '<sep>') + '\n'}).select(range(20))
+        if dataset_name == 'chinese-poetry-collection':
+            self.train_dataset = dataset_dict['train'].remap_columns({
+                'text1':
+                'src_txt'
+            }).select(range(20))
+            self.eval_dataset = dataset_dict['test'].remap_columns({
+                'text1':
+                'src_txt'
+            }).select(range(20))
+        self.tp = tp
+        self.work_dir = work_dir
+
+    def finetune(self, max_epochs, cfg_modify_fn):
+        kwargs = dict(
+            model='damo/nlp_gpt3_text-generation_1.3B',
+            train_dataset=self.train_dataset,
+            eval_dataset=self.eval_dataset,
+            max_epochs=max_epochs,
+            work_dir=self.work_dir,
+            cfg_modify_fn=cfg_modify_fn)
+        trainer = build_trainer(
+            name=Trainers.gpt3_trainer, default_args=kwargs)
+        trainer.train()
+
+    def evaluate(self):
+        kwargs = dict(
+            model=fr'{self.work_dir}/output',
+            eval_dataset=self.eval_dataset,
+            work_dir=self.work_dir)
+        trainer = build_trainer(default_args=kwargs)
+        trainer.evaluate()
+
+    def predict(self):
+        kwargs = dict(
+            model=fr'{self.work_dir}/output',
+            predict_datasets=self.eval_dataset,
+            work_dir=self.work_dir)
+        trainer = build_trainer(default_args=kwargs)
+        trainer.predict()
+
+    def pipeline(self):
+        pipeline_ins = pipeline(
+            Tasks.text_generation,
+            model=fr'{self.work_dir}/output',
+            work_dir=self.work_dir)
+        return pipeline_ins
+
+
+gpt3_one_layer_cw_tp_1 = GPT3OneLayerQAandCW(
+    dataset_name='chinese-poetry-collection',
+    tp=1,
+    work_dir='./gpt3_poetry_tp_1')
+
+
+def finetune_poetry_helper(max_epochs, gpt3_one_layer_qa_cw_obj):
+    max_epochs = max_epochs
+    num_warmup_steps = 100
+
+    def noam_lambda(current_step: int):
+        current_step += 1
+        return min(current_step**(-0.5),
+                   current_step * num_warmup_steps**(-1.5))
+
+    def cfg_modify_fn(cfg):
+        cfg.train.lr_scheduler = {
+            'type': 'LambdaLR',
+            'lr_lambda': noam_lambda,
+            'options': {
+                'by_epoch': False
+            }
+        }
+        cfg.train.optimizer = {'type': 'AdamW', 'lr': 3e-4}
+        cfg.train.dataloader = {'batch_size_per_gpu': 2, 'workers_per_gpu': 1}
+        cfg.train.hooks.append({'type': 'MegatronHook'})
+        cfg.evaluation.dataloader = {
+            'batch_size_per_gpu': 2,
+            'workers_per_gpu': 1
+        }
+        cfg.evaluation.metrics = 'ppl'
+        cfg.num_hidden_layers = 1
+        cfg.megatron.world_size = gpt3_one_layer_qa_cw_obj.tp
+        cfg.megatron.tensor_model_parallel_size = gpt3_one_layer_qa_cw_obj.tp
+        return cfg
+
+    gpt3_one_layer_qa_cw_obj.finetune(
+        max_epochs=max_epochs, cfg_modify_fn=cfg_modify_fn)
+
+
+def finetune_poetry_tp_1():
+    finetune_poetry_helper(
+        max_epochs=2, gpt3_one_layer_qa_cw_obj=gpt3_one_layer_cw_tp_1)
+
+
+def evaluate_poetry_tp_1():
+    gpt3_one_layer_cw_tp_1.evaluate()
+
+
+def predict_poetry_tp_1():
+    gpt3_one_layer_cw_tp_1.predict()
+
+
+def pipeline_poetry_helper(gpt3_one_layer_qa_cw_obj):
+    pipe = gpt3_one_layer_qa_cw_obj.pipeline()
+    input = '窗含西岭千秋雪'
+    gen_content = pipe(input, max_length=128)
+    with open(
+            fr'{gpt3_one_layer_qa_cw_obj.work_dir}/\
+            gpt3_1_layer_base_tp_{gpt3_one_layer_qa_cw_obj.tp}_cw_pipeline_gen_text.txt',
+            'w',
+            encoding='utf-8') as f:
+        f.write(gen_content)
+
+
+def pipeline_poetry_tp_1():
+    pipeline_poetry_helper(gpt3_one_layer_qa_cw_obj=gpt3_one_layer_cw_tp_1)
+
+
+gpt3_one_layer_cw_tp_2 = GPT3OneLayerQAandCW(
+    dataset_name='chinese-poetry-collection',
+    tp=2,
+    work_dir='./gpt3_poetry_tp_2')
+
+
+def finetune_poetry_tp_2():
+    finetune_poetry_helper(
+        max_epochs=2, gpt3_one_layer_qa_cw_obj=gpt3_one_layer_cw_tp_2)
+
+
+def evaluate_poetry_tp_2():
+    gpt3_one_layer_cw_tp_2.evaluate()
+
+
+def predict_poetry_tp_2():
+    gpt3_one_layer_cw_tp_2.predict()
+
+
+def pipeline_poetry_tp_2():
+    pipeline_poetry_helper(gpt3_one_layer_qa_cw_obj=gpt3_one_layer_cw_tp_2)
+
+
+gpt3_one_layer_qa_tp_1 = GPT3OneLayerQAandCW(
+    dataset_name='DuReader_robust-QG', tp=1, work_dir='./dureader_tp_1')
+
+
+def finetune_dureader_helper(max_epochs, gpt3_one_layer_qa_cw_obj):
+    max_epochs = max_epochs
+    num_warmup_steps = 100
+
+    def noam_lambda(current_step: int):
+        current_step += 1
+        return min(current_step**(-0.5),
+                   current_step * num_warmup_steps**(-1.5))
+
+    def cfg_modify_fn(cfg):
+        cfg.train.lr_scheduler = {
+            'type': 'LambdaLR',
+            'lr_lambda': noam_lambda,
+            'options': {
+                'by_epoch': False
+            }
+        }
+        cfg.train.optimizer = {'type': 'AdamW', 'lr': 3e-4}
+        cfg.train.dataloader = {'batch_size_per_gpu': 16, 'workers_per_gpu': 1}
+        cfg.train.hooks.append({
+            'type': 'EvaluationHook',
+            'by_epoch': True,
+            'interval': 1
+        })
+        cfg.train.hooks.append({'type': 'MegatronHook'})
+        cfg.num_hidden_layers = 1
+        cfg.preprocessor.sequence_length = 512
+        cfg.model.checkpoint_model_parallel_size = 1
+        cfg.megatron.world_size = gpt3_one_layer_qa_cw_obj.tp
+        cfg.megatron.tensor_model_parallel_size = gpt3_one_layer_qa_cw_obj.tp
+        return cfg
+
+    gpt3_one_layer_qa_cw_obj.finetune(
+        max_epochs=max_epochs, cfg_modify_fn=cfg_modify_fn)
+
+
+def finetune_dureader_tp_1():
+    finetune_dureader_helper(
+        max_epochs=2, gpt3_one_layer_qa_cw_obj=gpt3_one_layer_qa_tp_1)
+
+
+def evaluate_dureader_tp_1():
+    gpt3_one_layer_qa_tp_1.evaluate()
+
+
+def predict_dureader_tp_1():
+    gpt3_one_layer_qa_tp_1.predict()
+
+
+def pipeline_dureader_helper(gpt3_one_layer_qa_cw_obj):
+    pipe = gpt3_one_layer_qa_cw_obj.pipeline()
+    input1 = '推荐一下防脱发的洗发水'
+    input2 = '重新推荐一次'
+    gen_content1 = pipe(input1, max_length=128)
+    with open(
+            fr'{gpt3_one_layer_qa_cw_obj.work_dir}/\
+            gpt3_1_layer_base_tp_{gpt3_one_layer_qa_cw_obj.tp}_qa_pipeline_gen_text.txt',
+            'a+',
+            encoding='utf-8') as f:
+        f.write(gen_content1 + '\n')
+    gen_content2 = pipe(input2, max_length=128)
+    with open(
+            fr'{gpt3_one_layer_qa_cw_obj.work_dir}/\
+            gpt3_1_layer_base_tp_{gpt3_one_layer_qa_cw_obj.tp}_qa_pipeline_gen_text.txt',
+            'a+',
+            encoding='utf-8') as f:
+        f.write(gen_content2 + '\n')
+
+
+def pipeline_dureader_tp_1():
+    pipeline_dureader_helper(gpt3_one_layer_qa_tp_1)
+
+
+gpt3_one_layer_qa_tp_2 = GPT3OneLayerQAandCW(
+    dataset_name='DuReader_robust-QG', tp=2, work_dir='./dureader_tp_2')
+
+
+def finetune_dureader_tp_2():
+    finetune_dureader_helper(
+        max_epochs=2, gpt3_one_layer_qa_cw_obj=gpt3_one_layer_qa_tp_2)
+
+
+def evaluate_dureader_tp_2():
+    gpt3_one_layer_qa_tp_2.evaluate()
+
+
+def predict_dureader_tp_2():
+    gpt3_one_layer_qa_tp_2.predict()
+
+
+def pipeline_dureader_tp_2():
+    pipeline_dureader_helper(gpt3_one_layer_qa_tp_2)
+
+
+class CustomTestLoader(unittest.TestLoader):
+
+    def getTestCaseNames(self, testcase_class):
+        test_names = super().getTestCaseNames(testcase_class)
+        testcase_methods = list(testcase_class.__dict__.keys())
+        test_names.sort(key=testcase_methods.index)
+        return test_names
+
+
+if __name__ == '__main__':
+    unittest.main(testLoader=CustomTestLoader())
diff --git a/tests/trainers/test_gpt3_text-generation_chinese-base_ddp.py b/tests/trainers/test_gpt3_text-generation_chinese-base_ddp.py
new file mode 100644
index 00000000..b8f50ff2
--- /dev/null
+++ b/tests/trainers/test_gpt3_text-generation_chinese-base_ddp.py
@@ -0,0 +1,154 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import shutil
+import tempfile
+import unittest
+
+import torch
+
+from modelscope.metainfo import Trainers
+from modelscope.msdatasets import MsDataset
+from modelscope.pipelines import pipeline
+from modelscope.trainers import build_trainer
+from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import DistributedTestCase, test_level
+
+
+@unittest.skipIf(not torch.cuda.is_available()
+                 or torch.cuda.device_count() <= 1, 'distributed unittest')
+class TestGPT3BaseTrainDDP(DistributedTestCase):
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+        self.tmp_dir = tempfile.TemporaryDirectory().name
+        if not os.path.exists(self.tmp_dir):
+            os.makedirs(self.tmp_dir)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmp_dir)
+        super().tearDown()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_finetune_poetry(self):
+        self.start(finetune_poetry, num_gpus=4)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_gpt3_base_evaluate_poetry(self):
+        self.start(evaluate_poetry, num_gpus=4)
+
+    # TODO: add gpt3 trainer predict unittest
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_gpt3_base_predict_poetry(self):
+        self.start(predict_poetry, num_gpus=4)
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_gpt3_base_output_pipeline(self):
+        self.start(pipeline_gpt3_base_output, num_gpus=4)
+
+
+def finetune_poetry(work_dir='./gpt3_poetry'):
+    dataset_dict = MsDataset.load('chinese-poetry-collection')
+    train_dataset = dataset_dict['train'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(32))
+    eval_dataset = dataset_dict['test'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(32))
+    max_epochs = 2
+    tmp_dir = './gpt3_poetry'
+
+    num_warmup_steps = 100
+
+    def noam_lambda(current_step: int):
+        current_step += 1
+        return min(current_step**(-0.5),
+                   current_step * num_warmup_steps**(-1.5))
+
+    def cfg_modify_fn(cfg):
+        cfg.train.lr_scheduler = {
+            'type': 'LambdaLR',
+            'lr_lambda': noam_lambda,
+            'options': {
+                'by_epoch': False
+            }
+        }
+        cfg.train.optimizer = {'type': 'AdamW', 'lr': 3e-4}
+        cfg.train.dataloader = {'batch_size_per_gpu': 16, 'workers_per_gpu': 1}
+        cfg.evaluation.dataloader = {
+            'batch_size_per_gpu': 2,
+            'workers_per_gpu': 1
+        }
+        cfg.evaluation.metrics = 'ppl'
+        cfg.model.strict = False
+        return cfg
+
+    kwargs = dict(
+        model='damo/nlp_gpt3_text-generation_chinese-base',
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        max_epochs=max_epochs,
+        work_dir=tmp_dir,
+        cfg_modify_fn=cfg_modify_fn,
+        launcher='pytorch')
+
+    # Construct trainer and train
+    trainer = build_trainer(name=Trainers.gpt3_trainer, default_args=kwargs)
+    trainer.train()
+
+
+def evaluate_poetry(work_dir='./gpt3_poetry'):
+    dataset_dict = MsDataset.load('chinese-poetry-collection')
+    eval_dataset = dataset_dict['test'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(20))
+    tmp_dir = './gpt3_poetry'
+    kwargs = dict(
+        model=fr'{tmp_dir}/output',
+        eval_dataset=eval_dataset,
+        work_dir=tmp_dir,
+        launcher='pytorch')
+    trainer = build_trainer(default_args=kwargs)
+    trainer.evaluate()
+
+
+def predict_poetry(work_dir='./gpt3_poetry'):
+    dataset_dict = MsDataset.load('chinese-poetry-collection')
+    eval_dataset = dataset_dict['test'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(20))
+    tmp_dir = './gpt3_poetry'
+
+    kwargs = dict(
+        model=fr'{tmp_dir}/output',
+        predict_datasets=eval_dataset,
+        work_dir=tmp_dir,
+        launcher='pytorch')
+    trainer = build_trainer(default_args=kwargs)
+    trainer.predict()
+
+
+def pipeline_gpt3_base_output(work_dir='./gpt3_poetry'):
+    input = '窗含西岭千秋雪'
+    tmp_dir = './gpt3_poetry'
+    pipeline_ins = pipeline(
+        Tasks.text_generation, model=fr'{tmp_dir}/output', work_dir=tmp_dir)
+    gen_content = pipeline_ins(input, max_length=128)
+    with open(
+            fr'{work_dir}/nlp_gpt3_text-generation_chinese-base_pipeline_gen_text.txt',
+            'w',
+            encoding='utf-8') as f:
+        f.write(gen_content)
+
+
+class CustomTestLoader(unittest.TestLoader):
+
+    def getTestCaseNames(self, testcase_class):
+        test_names = super().getTestCaseNames(testcase_class)
+        testcase_methods = list(testcase_class.__dict__.keys())
+        test_names.sort(key=testcase_methods.index)
+        return test_names
+
+
+if __name__ == '__main__':
+    unittest.main(testLoader=CustomTestLoader())
diff --git a/tests/trainers/test_gpt3_text-generation_chinese-base_train.py b/tests/trainers/test_gpt3_text-generation_chinese-base_train.py
new file mode 100644
index 00000000..07820a77
--- /dev/null
+++ b/tests/trainers/test_gpt3_text-generation_chinese-base_train.py
@@ -0,0 +1,152 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import os
+import shutil
+import tempfile
+import unittest
+
+import torch
+
+from modelscope.metainfo import Trainers
+from modelscope.msdatasets import MsDataset
+from modelscope.pipelines import pipeline
+from modelscope.trainers import build_trainer
+from modelscope.utils.constant import Tasks
+from modelscope.utils.test_utils import DistributedTestCase, test_level
+
+
+@unittest.skipIf(not torch.cuda.is_available()
+                 or torch.cuda.device_count() <= 1, 'distributed unittest')
+class TestGPT3BaseTrain(DistributedTestCase):
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+
+        self.tmp_dir = tempfile.TemporaryDirectory().name
+        if not os.path.exists(self.tmp_dir):
+            os.makedirs(self.tmp_dir)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmp_dir)
+        super().tearDown()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_finetune_poetry(self):
+        finetune_poetry()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_gpt3_base_evaluate_poetry(self):
+        evaluate_poetry()
+
+    # TODO: add gpt3 trainer predict unittest
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_gpt3_base_predict_poetry(self):
+        predict_poetry()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_gpt3_base_output_pipeline(self):
+        pipeline_gpt3_base_output()
+
+
+def finetune_poetry(work_dir='./gpt3_poetry'):
+    dataset_dict = MsDataset.load('chinese-poetry-collection')
+    train_dataset = dataset_dict['train'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(20))
+    eval_dataset = dataset_dict['test'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(20))
+    max_epochs = 2
+    tmp_dir = './gpt3_poetry'
+
+    num_warmup_steps = 100
+
+    def noam_lambda(current_step: int):
+        current_step += 1
+        return min(current_step**(-0.5),
+                   current_step * num_warmup_steps**(-1.5))
+
+    def cfg_modify_fn(cfg):
+        cfg.train.lr_scheduler = {
+            'type': 'LambdaLR',
+            'lr_lambda': noam_lambda,
+            'options': {
+                'by_epoch': False
+            }
+        }
+        cfg.train.optimizer = {'type': 'AdamW', 'lr': 3e-4}
+        cfg.train.dataloader = {'batch_size_per_gpu': 2, 'workers_per_gpu': 1}
+        cfg.evaluation.dataloader = {
+            'batch_size_per_gpu': 2,
+            'workers_per_gpu': 1
+        }
+        cfg.evaluation.metrics = 'ppl'
+        cfg.model.strict = False
+        return cfg
+
+    kwargs = dict(
+        model='damo/nlp_gpt3_text-generation_chinese-base',
+        train_dataset=train_dataset,
+        eval_dataset=eval_dataset,
+        max_epochs=max_epochs,
+        work_dir=tmp_dir,
+        cfg_modify_fn=cfg_modify_fn)
+
+    # Construct trainer and train
+    trainer = build_trainer(name=Trainers.gpt3_trainer, default_args=kwargs)
+    trainer.train()
+
+
+def evaluate_poetry(work_dir='./gpt3_poetry'):
+    dataset_dict = MsDataset.load('chinese-poetry-collection')
+    eval_dataset = dataset_dict['test'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(20))
+    tmp_dir = './gpt3_poetry'
+    kwargs = dict(
+        model=fr'{tmp_dir}/output',
+        eval_dataset=eval_dataset,
+        work_dir=tmp_dir)
+    trainer = build_trainer(default_args=kwargs)
+    trainer.evaluate()
+
+
+def predict_poetry(work_dir='./gpt3_poetry'):
+    dataset_dict = MsDataset.load('chinese-poetry-collection')
+    eval_dataset = dataset_dict['test'].remap_columns({
+        'text1': 'src_txt'
+    }).select(range(20))
+    tmp_dir = './gpt3_poetry'
+
+    kwargs = dict(
+        model=fr'{tmp_dir}/output',
+        predict_datasets=eval_dataset,
+        work_dir=tmp_dir)
+    trainer = build_trainer(default_args=kwargs)
+    trainer.predict()
+
+
+def pipeline_gpt3_base_output(work_dir='./gpt3_poetry'):
+    input = '窗含西岭千秋雪'
+    tmp_dir = './gpt3_poetry'
+    pipeline_ins = pipeline(
+        Tasks.text_generation, model=fr'{tmp_dir}/output', work_dir=tmp_dir)
+    gen_content = pipeline_ins(input, max_length=128)
+    with open(
+            fr'{work_dir}/nlp_gpt3_text-generation_chinese-base_pipeline_gen_text.txt',
+            'w',
+            encoding='utf-8') as f:
+        f.write(gen_content)
+
+
+class CustomTestLoader(unittest.TestLoader):
+
+    def getTestCaseNames(self, testcase_class):
+        test_names = super().getTestCaseNames(testcase_class)
+        testcase_methods = list(testcase_class.__dict__.keys())
+        test_names.sort(key=testcase_methods.index)
+        return test_names
+
+
+if __name__ == '__main__':
+    unittest.main(testLoader=CustomTestLoader())