初始化项目，由ModelHub XC社区提供模型

Model: Nobitaxi/InternLM2-chat-7B-SQL
Source: Original Platform

README.md

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+###### Clone with HTTP
+```
+git clone https://www.modelscope.cn/Nobitaxi/InternLM2-chat-7B-SQL.git
+```
+# \<InternLM2-chat-7B-SQL>介绍
+本模型是基于InternLM-chat-7B，在19,5297条数据上进行3个epoch的FineTune训练出来的关于Text-to-SQL领域的模型。
+
+## 模型描述
+
+本模型根据自然语言问句及数据库表结构生成对应的SQL。
+
+## 训练数据介绍
+
+使用DB-GPT处理并在Hugging Face开源的数据集，经过筛除掉多轮对话数据以及整理格式后得到19,5297条数据。
+
+处理后的格式如下：
+
+```
+[
+  {
+    "question": "which states border arizona",
+    "context": "CREATE TABLE mountain (mountain_name, mountain_altitude, state_name, country_name); CREATE TABLE city (city_name, state_name, population, country_name); CREATE TABLE road (road_name, state_name); CREATE TABLE border_info (state_name, border); CREATE TABLE river (river_name, length, traverse, country_name); CREATE TABLE state (state_name, capital, population, area, country_name, density); CREATE TABLE highlow (state_name, highest_point, highest_elevation, lowest_point, lowest_elevation); CREATE TABLE lake (lake_name, area, state_name, country_name)",
+    "answer": "SELECT border FROM border_info WHERE state_name = 'arizona'"
+  },
+  ...
+  {}
+]
+```
+
+## 模型训练流程
+
+本项目使用xtuner0.1.15, 在internlm2-chat-7b上进行微调, [模型地址](https://www.modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary)
+
+1. 使用xtuner train进行训练
+
+   ```
+   xtuner train ${YOUR_CONFIG} --deepspeed deepseed_zero2
+   ```
+
+2. 将训练后得到的.pth模型转换为HuggingFace模型: **即:生成Adapter文件**
+
+   ```
+   export MKL_SERVICE_FORCE_INTEL=1
+   xtuner convert pth_to_hf ${YOUR_CONFIG} ${PTH} ${ADAPTER_PATH}
+   ```
+
+3. 将HuggingFace adapter合并到训练的基座模型中
+
+   ```
+   xtuner convert merge ${BASE_LLM_PATH} ${ADAPTER_PATH} ${SAVE_PATH} --max-shard-size 2GB
+   ```
+
+## 如何使用
+编写一个cli_demo.py脚本用来简单使用
+```
+import torch
+from modelscope import AutoTokenizer, AutoModelForCausalLM
+
+
+model_name_or_path = "Nobitaxi/InternLM2-chat-7B-SQL"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto')
+model = model.eval()
+
+system_prompt = """If you are an expert in SQL, please generate a good SQL Query for Question based on the CREATE TABLE statement."""
+
+messages = [(system_prompt, '')]
+
+print("=============Welcome to InternLM2-chat-7b-sql chatbot, type 'exit' to exit.=============")
+
+while True:
+    input_text = input("User  >>> ")
+    input_text.replace(' ', '')
+    if input_text == "exit":
+        break
+    response, history = model.chat(tokenizer, input_text, history=messages)
+    messages.append((input_text, response))
+    print(f"robot >>> {response}")
+```
+**注意：输入中需要包含问句及数据库模式，训练模型时，数据集中的数据库模式用建表语句表示；体验时可以额外添加更多上下文信息以提升模型生成SQL的准确率**
+
+---
+
+**其他内容, 后续补充~**
+