diff --git a/README.md b/README.md
index e0a056a..581342a 100644
--- a/README.md
+++ b/README.md
@@ -397,3 +397,34 @@ print(f'Onnx-support/Total Models: {len(onnx_list)}/{len(full_list)}')
 ```
     2022-12-13 16:25:15,916 - 140704500614336 - auto_transformers.py-auto_transformers:68 - WARNING: The operator is initialized without specified model.
     Onnx-support/Total Models: 111/126
+
+## Fine-tune
+### Get start
+We have prepared some most typical use of [finetune examples](https://github.com/towhee-io/examples/tree/main/fine_tune/6_train_language_modeling_tasks).
+
+Simply speaking, you only need to construct an op instance and pass in some configurations to train the specified task.
+```python
+import towhee
+
+bert_op = towhee.ops.text_embedding.transformers(model_name='bert-base-uncased').get_op()
+data_args = {
+    'dataset_name': 'wikitext',
+    'dataset_config_name': 'wikitext-2-raw-v1',
+}
+training_args = {
+    'num_train_epochs': 3, # you can add epoch number to get a better metric.
+    'per_device_train_batch_size': 8,
+    'per_device_eval_batch_size': 8,
+    'do_train': True,
+    'do_eval': True,
+    'output_dir': './tmp/test-mlm',
+    'overwrite_output_dir': True
+}
+bert_op.train(task='mlm', data_args=data_args, training_args=training_args)
+
+```
+For more infos, refer to the [examples](https://github.com/towhee-io/examples/tree/main/fine_tune/6_train_language_modeling_tasks).
+
+### Dive deep and customize your training
+You can change the [training script](https://towhee.io/text-embedding/transformers/src/branch/main/train_clm_with_hf_trainer.py) in your customer way. 
+Or your can refer to the original [hugging face transformers training examples](https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling).
\ No newline at end of file