docs/组队学习/大模型实战/TASK2.md · 黑桃/my_blog

基础作业：

- 使用 InternLM-Chat-7B 模型生成 300字的小故事（需截图）。
- 熟悉 hugging face 下载功能，使用
   huggingface_hub python包，下载
   InternLM-20B 的 config.json 文件到本地（需截图下载过程）。

进阶作业（可选做）

- 完成浦语 •灵笔的图文理解及创作部署（需截图）
- 完成 Lagent 工具调用 Demo 创作部署（需截图）

## **InternLM-Chat-7B** 智能对话 **Demo**

### 什么是大模型？

**参数数量巨大**、拥有**庞大计算能力和参数规模**的模型

### **特点及应用**

- 利用大量数据进行训练，
- 拥有数十亿甚至数千亿个参数
- 在各种任务中展现出惊人的性能
- 通常采用深度神经网络结构，如 `Transformer`、`BERT`、`GPT`（ Generative Pre-trained Transformer ）等。

- 优点  
  - 能够捕捉和理解数据中更为复杂、抽象的特征和关系。
  - 通过大规模参数的学习，它们可以提高在各种任务上的泛化能力，并在未经过大量特定领域数据训练的情况下实现较好的表现。
- 缺点
  - 巨大的计算资源需求、高昂的训练成本、
  - 对大规模数据的依赖以及模型的可解释性等问题。
  - 需要在性能、成本和道德等多个方面进行权衡和考量。

### InternLM 模型全链条开源

#### InternLM

- `InternLM` 是一个开源的轻量级训练框架
- 支持大模型训练而无需大量的依赖。
- 通过单一的代码库，它支持在拥有数千个 `GPU` 的大型集群上进行预训练，
- 支持单个 `GPU` 上进行微调
- 在 `1024` 个 `GPU` 上训练时，`InternLM` 可以实现近 `90%` 的加速效率。
- 两个开源的预训练模型
  - `InternLM-7B` 和 `InternLM-20B`(上海人工智能实验室)。

#### Lagent

- `Lagent` 是一个轻量级、开源的基于大语言模型的智能体（agent）框架

- 支持用户快速地将一个大语言模型转变为多种类型的智能体，

![Lagent 框架图](img/TASK2/Lagent.png)

#### 浦语·灵笔

- 基于书生·浦语大语言模型研发的视觉-语言大模型
- 提供出色的图文理解和创作能力

### 环境准备

### 创建开发机

![image-20240102214122231](img/TASK2/image-20240102214122231.png)

### 进入开发机

![image-20240102214842122](img/TASK2/image-20240102214842122.png)

### 克隆环境并激活&安装依赖包

```
bash # 请每次使用 jupyter lab 打开终端时务必先执行 bash 命令进入 bash 中 conda create --name internlm-demo --clone=/root/share/conda_envs/internlm-base
conda activate internlm-demo 
python -m pip install --upgrade pip
pip install modelscope==1.9.5
pip install transformers==4.35.2
pip install streamlit==1.24.0
pip install sentencepiece==0.1.99
pip install accelerate==0.24.1
```

### 模型下载

```
mkdir -p /root/model/Shanghai_AI_Laboratory
cp -r /root/share/temp/model_repos/internlm-chat-7b /root/model/Shanghai_AI_Laboratory
```

### 新建下载文件

`vim download.py`

```
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b', cache_dir='/root/model/Shanghai_AI_Laboratory/', revision='v1.0.3')
```

### clone 代码

```
mkdir -p /root/code
cd /root/code
git clone https://gitee.com/internlm/InternLM.git
cd InternLM
git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17
```

### 更换模型地址

```
cd /root/code
sed -i  s/internlm/Shanghai_AI_Laboratory/g web_demo.py
sed -i  s/Shanghai_AI_Laboratory-chat-7b/internlm-chat-7b/g web_demo.py 
```

### 运行

#### 终端运行

````
cd /root/code/InternLM
vim cli_demo.py
````

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name_or_path = "/root/model/Shanghai_AI_Laboratory/internlm-chat-7b"
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()
messages = []
print("=============Welcome to InternLM chatbot, type 'exit' toexit.=============")
while True:
    input_text = input("User  >>> ")
    if input_text == "exit":
       break
    response, history = model.chat(tokenizer, input_text,history=messages)
    messages.append((input_text, response))
    print(f"robot >>> {response}")
```

```
python /root/code/InternLM/cli_demo.py
```

![image-20240102221752913](img/TASK2/image-20240102221752913.png)

#### **web demo** 运行

```
bash
conda activate internlm-demo # 首次进入 vscode 会默认是 base 环境，所以首 先切换环境
cd /root/code/InternLM
## 远程机器启动服务
streamlit run web_demo.py --server.address 127.0.0.1 --server.port 6006
```

#### 添加公钥

![image-20240102223546528](img/TASK2/image-20240102223546528.png)

![image-20240102223515047](img/TASK2/image-20240102223515047.png)

#### 查看机器对应端口号

![image-20240102230600080](img/TASK2/image-20240102230600080.png)

#### 本地机器开端口映射

```
ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 33531

```

#### 登录浏览器

http://127.0.0.1:6006/

![image-20240102230317076](img/TASK2/image-20240102230317076.png)

### 写一个小故事

![image-20240107102635197](img/TASK2//image-20240107102635197.png)

## **Lagent** 智能体工具调用 **Demo**

### 环境准备

### **Lagent** 安装

```
cd /root/code
git clone https://gitee.com/internlm/lagent.git
cd /root/code/lagent
git checkout 511b03889010c4811b1701abb153e02b8e94fb5e # 尽量保证和教程 commit版本一致
pip install -e . # 源码安装
```

![image-20240102231447341](img/TASK2/image-20240102231447341.png)

修改代码

```
react_web_demo.py
```

```
import copy
import os

import streamlit as st
from streamlit.logger import get_logger

from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
from lagent.agents.react import ReAct
from lagent.llms import GPTAPI
from lagent.llms.huggingface import HFTransformerCasualLM

class SessionState:

def init_state(self):
        """Initialize session state variables."""
        st.session_state['assistant'] = []
        st.session_state['user'] = []

#action_list = [PythonInterpreter(), GoogleSearch()]
        action_list = [PythonInterpreter()]
        st.session_state['plugin_map'] = {
            action.name: action
            for action in action_list
        }
        st.session_state['model_map'] = {}
        st.session_state['model_selected'] = None
        st.session_state['plugin_actions'] = set()

def clear_state(self):
        """Clear the existing session state."""
        st.session_state['assistant'] = []
        st.session_state['user'] = []
        st.session_state['model_selected'] = None
        if 'chatbot' in st.session_state:
            st.session_state['chatbot']._session_history = []

class StreamlitUI:

def __init__(self, session_state: SessionState):
        self.init_streamlit()
        self.session_state = session_state

def init_streamlit(self):
        """Initialize Streamlit's UI settings."""
        st.set_page_config(
            layout='wide',
            page_title='lagent-web',
            page_icon='./docs/imgs/lagent_icon.png')
        # st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
        st.sidebar.title('模型控制')

def setup_sidebar(self):
        """Setup the sidebar for model and plugin selection."""
        model_name = st.sidebar.selectbox(
            '模型选择：', options=['gpt-3.5-turbo','internlm'])
        if model_name != st.session_state['model_selected']:
            model = self.init_model(model_name)
            self.session_state.clear_state()
            st.session_state['model_selected'] = model_name
            if 'chatbot' in st.session_state:
                del st.session_state['chatbot']
        else:
            model = st.session_state['model_map'][model_name]

plugin_name = st.sidebar.multiselect(
            '插件选择',
            options=list(st.session_state['plugin_map'].keys()),
            default=[list(st.session_state['plugin_map'].keys())[0]],
        )

plugin_action = [
            st.session_state['plugin_map'][name] for name in plugin_name
        ]
        if 'chatbot' in st.session_state:
            st.session_state['chatbot']._action_executor = ActionExecutor(
                actions=plugin_action)
        if st.sidebar.button('清空对话', key='clear'):
            self.session_state.clear_state()
        uploaded_file = st.sidebar.file_uploader(
            '上传文件', type=['png', 'jpg', 'jpeg', 'mp4', 'mp3', 'wav'])
        return model_name, model, plugin_action, uploaded_file

def init_model(self, option):
        """Initialize the model based on the selected option."""
        if option not in st.session_state['model_map']:
            if option.startswith('gpt'):
                st.session_state['model_map'][option] = GPTAPI(
                    model_type=option)
            else:
                st.session_state['model_map'][option] = HFTransformerCasualLM(
                    '/root/model/Shanghai_AI_Laboratory/internlm-chat-7b')
        return st.session_state['model_map'][option]

def initialize_chatbot(self, model, plugin_action):
        """Initialize the chatbot with the given model and plugin actions."""
        return ReAct(
            llm=model, action_executor=ActionExecutor(actions=plugin_action))

def render_user(self, prompt: str):
        with st.chat_message('user'):
            st.markdown(prompt)

def render_assistant(self, agent_return):
        with st.chat_message('assistant'):
            for action in agent_return.actions:
                if (action):
                    self.render_action(action)
            st.markdown(agent_return.response)

def render_action(self, action):
 with st.expander(action.type, expanded=True):
 st.markdown(
 " 插 件:" # noqa E501
 + action.type + '',
 unsafe_allow_html=True)
 st.markdown(
 " 思考步骤:" # noqa E501
 + action.thought + '',
 unsafe_allow_html=True)
 if (isinstance(action.args, dict) and 'text' in action.args):
 st.markdown(
 " 执行内容:", # noqa E501
 unsafe_allow_html=True)
 st.markdown(action.args['text'])
 self.render_action_results(action)

def render_action_results(self, action):
 """Render the results of action, including text, images, videos, and
 audios."""
 if (isinstance(action.result, dict)):
 st.markdown(
 " 执行结果:", # noqa E501
 unsafe_allow_html=True)
 if 'text' in action.result:
 st.markdown(
 "" + action.result['text'] +
 '',
 unsafe_allow_html=True)
 if 'image' in action.result:
 image_path = action.result['image']
 image_data = open(image_path, 'rb').read()
 st.image(image_data, caption='Generated Image')
 if 'video' in action.result:
 video_data = action.result['video']
 video_data = open(video_data, 'rb').read()
 st.video(video_data)
 if 'audio' in action.result:
 audio_data = action.result['audio']
 audio_data = open(audio_data, 'rb').read()
 st.audio(audio_data)

def main():
    logger = get_logger(__name__)
    # Initialize Streamlit UI and setup sidebar
    if 'ui' not in st.session_state:
        session_state = SessionState()
        session_state.init_state()
        st.session_state['ui'] = StreamlitUI(session_state)

else:
        st.set_page_config(
            layout='wide',
            page_title='lagent-web',
            page_icon='./docs/imgs/lagent_icon.png')
        # st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
    model_name, model, plugin_action, uploaded_file = st.session_state[
        'ui'].setup_sidebar()

# Initialize chatbot if it is not already initialized
    # or if the model has changed
    if 'chatbot' not in st.session_state or model != st.session_state[
            'chatbot']._llm:
        st.session_state['chatbot'] = st.session_state[
            'ui'].initialize_chatbot(model, plugin_action)

for prompt, agent_return in zip(st.session_state['user'],
                                    st.session_state['assistant']):
        st.session_state['ui'].render_user(prompt)
        st.session_state['ui'].render_assistant(agent_return)
    # User input form at the bottom (this part will be at the bottom)
    # with st.form(key='my_form', clear_on_submit=True):

if user_input := st.chat_input(''):
        st.session_state['ui'].render_user(user_input)
        st.session_state['user'].append(user_input)
        # Add file uploader to sidebar
        if uploaded_file:
            file_bytes = uploaded_file.read()
            file_type = uploaded_file.type
            if 'image' in file_type:
                st.image(file_bytes, caption='Uploaded Image')
            elif 'video' in file_type:
                st.video(file_bytes, caption='Uploaded Video')
            elif 'audio' in file_type:
                st.audio(file_bytes, caption='Uploaded Audio')
            # Save the file to a temporary location and get the path
            file_path = os.path.join(root_dir, uploaded_file.name)
            with open(file_path, 'wb') as tmpfile:
                tmpfile.write(file_bytes)
            st.write(f'File saved at: {file_path}')
            user_input = '我上传了一个图像，路径为: {file_path}. {user_input}'.format(
                file_path=file_path, user_input=user_input)
        agent_return = st.session_state['chatbot'].chat(user_input)
        st.session_state['assistant'].append(copy.deepcopy(agent_return))
        logger.info(agent_return.inner_steps)
        st.session_state['ui'].render_assistant(agent_return)

if __name__ == '__main__':
    root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
    root_dir = os.path.join(root_dir, 'tmp_dir')
    os.makedirs(root_dir, exist_ok=True)
    main()
```

```
streamlit run /root/code/lagent/examples/react_web_demo.py --server.address 127.0.0.1 --server.port 6006
```

### 解决数学问题

![image-20240107105612614](img/TASK2//image-20240107105612614.png)

## 浦语**·**灵笔图文理解创作 Demo

### 环境准备

```
bash
conda create --name xcomposer-demo --clone=/root/share/conda_envs/internlm-base
conda activate xcomposer-demo
pip install transformers==4.33.1 timm==0.4.12 sentencepiece==0.1.99 gradio==3.44.4 markdown2==2.4.10 xlsxwriter==3.1.2 einops accelerate
pip install modelscope==1.9.5
```

### 模型下载

```
mkdir -p /root/model/Shanghai_AI_Laboratory
cp -r /root/share/temp/model_repos/internlm-xcomposer-7b /root/model/Shanghai_AI_Laboratory
```

```
vim download.py
```

```
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-xcomposer-7b', cache_dir='/root/model', revision='master')
```

### Clone 代码

```
cd /root/code
git clone https://gitee.com/internlm/InternLM-XComposer.git
cd /root/code/InternLM-XComposer
git checkout 3e8c79051a1356b9c388a6447867355c0634932d # 最好保证和教程的 commit 版本一致
```

### **Demo** 运行

```
cd /root/code/InternLM-XComposer
python examples/web_demo.py  --folder /root/model/Shanghai_AI_Laboratory/internlm-xcomposer-7b --num_gpus 1  --port 6006
```

### 登录浏览器

#### 创作图文测试

![image-20240103001453010](img/TASK2/image-20240103001453010.png)

#### 多模态对话测试

![image-20240103001903682](img/TASK2/image-20240103001903682.png)

[简洁而优雅地展示你的算法和数据——streamlit教程（一） 原理介绍与布局控制 - 锦恢的文章 - 知乎](

## 模型下载

### 使用huggingface下载模型

安装依赖

````
pip install -U huggingface_hub
````

创建python下载脚本

````
import os

# 下载模型
os.system('huggingface-cli download --resume-download internlm/internlm-chat-20b --local-dir /root/model')
--resume-download 断点续下
--local-dir 本地存储路径
````

HF镜像站：https://hf-mirror.com

````
export HF_ENDPOINT=https://hf-mirror.com #修改环境变量 
huggingface-cli download --resume-download internlm/internlm-chat-20b --local-dir /root/model 
````

下载模型

![image-20240107114433806](img/TASK2//image-20240107114433806.png)

下载模型中的部分文件

````
import os 
from huggingface_hub import hf_hub_download  # Load model directly

hf_hub_download(repo_id="internlm/internlm-7b", filename="config.json")
````

![image-20240107125141814](img/TASK2//image-20240107125141814.png)

### 使用ModelScope下载模型

安装依赖：

```
pip install modelscope==1.9.5
pip install transformers==4.35.2
```

在当前目录下新建 python 文件，填入以下代码，运行即可。

```
import torch
from modelscope import snapshot_download, AutoModel, AutoTokenizer
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-20b', cache_dir='/root/model', revision='master')
```

下载模型

![image-20240107124859618](img/TASK2//image-20240107124859618.png)

### 使用 OpenXLab下载模型

OpenXLab 可以通过指定模型仓库的地址，以及需要下载的文件的名称，文件所需下载的位置等，直接下载模型权重文件。

使用python脚本下载模型首先要安装依赖，安装代码如下：`pip install -U openxlab` 安装完成后使用 download 函数导入模型中心的模型。

在当前目录下新建 python 文件，填入以下代码，运行即可。

````
from openxlab.model import download
download(model_repo='OpenLMLab/InternLM-20b', model_name='InternLM-20b', output='/root/model')
````

课程视频上新啦~ ，今天带小伙伴们跑通大模型领域的 hello world，轻松玩转书生·浦语大模型的 3 个趣味 demo（InternLM-Chat-7B 智能对话、Lagent工具调用解简单数学题、浦语·灵笔多模态图文创作和理解）
视频：https://www.bilibili.com/video/BV1Ci4y1z72H/
文档：https://github.com/InternLM/tutorial/blob/main/helloworld/hello_world.md

第二次作业在文档末尾，提交地址为https://github.com/InternLM/tutorial/discussions/98，

第二次笔记提交地址https://github.com/InternLM/tutorial/discussions/97。

第一次课程链接:https://www.bilibili.com/video/BV1Rc411b7ns/，

第一次笔记链接:https://github.com/InternLM/tutorial/discussions/54

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