Update README.zh.md

README.zh.md

@@ -35,12 +35,13 @@
     - [2.2、基座模型](#22基座模型)
   - [三、使用方法](#三使用方法)
     - [3.1、环境准备](#31环境准备)
-    - [3.2、数据准备](#32数据准备)
-    - [3.3、模型微调](#33模型微调)
-    - [3.4、模型预测](#34模型预测)
-    - [3.5、模型权重](#35模型权重)
-      - [3.5.1 模型和微调权重合并](#351-模型和微调权重合并)
-    - [3.6、模型评估](#36模型评估)
+    - [3.2、快速开始](#32快速开始)
+    - [3.3、数据准备](#33数据准备)
+    - [3.4、模型微调](#34模型微调)
+    - [3.5、模型预测](#35模型预测)
+    - [3.6、模型权重](#36模型权重)
+      - [3.6.1 模型和微调权重合并](#361-模型和微调权重合并)
+    - [3.7、模型评估](#37模型评估)
   - [四、发展路线](#四发展路线)
   - [五、贡献](#五贡献)
   - [六、感谢](#六感谢)
@@ -136,6 +137,96 @@ poetry run sh dbgpt_hub/scripts/gen_train_eval_data.sh
 ```     
 项目的数据处理代码中已经嵌套了`chase` 、`cosql`、`sparc`的数据处理，可以根据上面链接将数据集下载到data路径后，在`dbgpt_hub/configs/config.py`中将 `SQL_DATA_INFO`中对应的代码注释松开即可。  
 
+### 3.2 快速开始
+
+首先，用如下命令安装`dbgpt-hub`：
+
+`pip install dbgpt-hub`
+
+然后，指定参数并用几行代码完成整个Text2SQL fine-tune流程：
+```python
+from dbgpt_hub.data_process import preprocess_sft_data
+from dbgpt_hub.train import start_sft
+from dbgpt_hub.predict import start_predict
+from dbgpt_hub.eval import start_evaluate
+
+# 配置训练和验证集路径和参数
+data_folder = "dbgpt_hub/data"
+data_info = [
+        {
+            "data_source": "spider",
+            "train_file": ["train_spider.json", "train_others.json"],
+            "dev_file": ["dev.json"],
+            "tables_file": "tables.json",
+            "db_id_name": "db_id",
+            "is_multiple_turn": False,
+            "train_output": "spider_train.json",
+            "dev_output": "spider_dev.json",
+        }
+]
+
+# 配置fine-tune参数
+train_args = {
+            "model_name_or_path": "codellama/CodeLlama-13b-Instruct-hf",
+            "do_train": True,
+            "dataset": "example_text2sql_train",
+            "max_source_length": 2048,
+            "max_target_length": 512,
+            "finetuning_type": "lora",
+            "lora_target": "q_proj,v_proj",
+            "template": "llama2",
+            "lora_rank": 64,
+            "lora_alpha": 32,
+            "output_dir": "dbgpt_hub/output/adapter/CodeLlama-13b-sql-lora",
+            "overwrite_cache": True,
+            "overwrite_output_dir": True,
+            "per_device_train_batch_size": 1,
+            "gradient_accumulation_steps": 16,
+            "lr_scheduler_type": "cosine_with_restarts",
+            "logging_steps": 50,
+            "save_steps": 2000,
+            "learning_rate": 2e-4,
+            "num_train_epochs": 8,
+            "plot_loss": True,
+            "bf16": True,
+}
+
+# 配置预测参数
+predict_args = {
+            "model_name_or_path": "codellama/CodeLlama-13b-Instruct-hf",
+            "template": "llama2",
+            "finetuning_type": "lora",
+            "checkpoint_dir": "dbgpt_hub/output/adapter/CodeLlama-13b-sql-lora",
+            "predict_file_path": "dbgpt_hub/data/eval_data/dev_sql.json",
+            "predict_out_dir": "dbgpt_hub/output/",
+            "predicted_out_filename": "pred_sql.sql",
+}
+
+# 配置评估参数
+evaluate_args =  {
+            "input": "./dbgpt_hub/output/pred/pred_sql_dev_skeleton.sql",
+            "gold": "./dbgpt_hub/data/eval_data/gold.txt",
+            "gold_natsql": "./dbgpt_hub/data/eval_data/gold_natsql2sql.txt",
+            "db": "./dbgpt_hub/data/spider/database",
+            "table": "./dbgpt_hub/data/eval_data/tables.json",
+            "table_natsql": "./dbgpt_hub/data/eval_data/tables_for_natsql2sql.json",
+            "etype": "exec",
+            "plug_value": True,
+            "keep_distict": False,
+            "progress_bar_for_each_datapoint": False,
+            "natsql": False,
+}
+
+# 执行整个Fine-tune流程
+preprocess_sft_data(
+      data_folder = data_folder,
+      data_info = data_info
+)
+
+start_sft(train_args)
+start_predict(predict_args)
+start_evaluate(evaluate_args)
+```
 
 ### 3.3、模型微调