大模型及InternLM基本介绍

大模型

• 定义：参数量巨大、拥有庞大计算能力和参数规模的模型
• 特点：大量数据训练、数十亿甚至千亿数据、惊人性能

InternLM系列

• InternLM：轻量级训练框架
• Lagent：轻量级、开源的基于大语言模型得到智能体框架，将大语言模型转变为多种智能体
• 浦语灵笔：视觉语言大模型，出色的图文理解和图文创作能力
• InternLM-7B：70亿参数，支持8k token

实战部分

demo部署

准备工作

白嫖A100

• 克隆环境

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
bash /root/share/install_conda_env_internlm_base.sh internlm-demo  # 执行该脚本文件来安装项目实验环境

• 激活环境

conda activate internlm-demo

• 安装依赖

# 升级pip
python -m pip install --upgrade pippip 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

• 复制模型

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

代码准备

• clone代码

cd /root/code
git clone https://gitee.com/internlm/InternLM.git

保证版本一致

cd InternLM
git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17

终端运行

在 /root/code/InternLM 目录下新建一个 cli_demo.py 文件，将以下代码填入其中

import torch
from transformers import AutoTokenizer, AutoModelForCausalLMmodel_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()system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文.
"""messages = [(system_prompt, '')]print("=============Welcome to InternLM chatbot, type 'exit' to exit.=============")while True:input_text = input("User  >>> ")input_text = input_text.replace(' ', '')if input_text == "exit":breakresponse, history = model.chat(tokenizer, input_text, history=messages)messages.append((input_text, response))print(f"robot >>> {response}")

运行代码

python /root/code/InternLM/cli_demo.py

web demo 运行

配置SSH本地端口，即可在网页上使用，效果如图
在本地运行，

ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 34683

并在浏览器打开

http://127.0.0.1:6006

在服务器端，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
streamlit run web_demo.py --server.address 127.0.0.1 --server.port 6006

效果如图

Lagent 智能体工具调用 Demo

准备工作

环境准备

# 升级pip
python -m pip install --upgrade pippip 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

安装 Lagent

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

替换/root/code/lagent/examples/react_web_demo.py的代码

import copy
import osimport streamlit as st
from streamlit.logger import get_loggerfrom lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
from lagent.agents.react import ReAct
from lagent.llms import GPTAPI
from lagent.llms.huggingface import HFTransformerCasualLMclass 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: actionfor action in action_list}st.session_state['model_map'] = {}st.session_state['model_selected'] = Nonest.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'] = Noneif '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_statedef 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_nameif '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_filedef 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("<p style='text-align: left;display:flex;'> <span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'>插    件</span><span style='width:14px;text-align:left;display:block;'>:</span><span style='flex:1;'>"  # noqa E501+ action.type + '</span></p>',unsafe_allow_html=True)st.markdown("<p style='text-align: left;display:flex;'> <span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'>思考步骤</span><span style='width:14px;text-align:left;display:block;'>:</span><span style='flex:1;'>"  # noqa E501+ action.thought + '</span></p>',unsafe_allow_html=True)if (isinstance(action.args, dict) and 'text' in action.args):st.markdown("<p style='text-align: left;display:flex;'><span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'> 执行内容</span><span style='width:14px;text-align:left;display:block;'>:</span></p>",  # noqa E501unsafe_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, andaudios."""if (isinstance(action.result, dict)):st.markdown("<p style='text-align: left;display:flex;'><span style='font-size:14px;font-weight:600;width:70px;text-align-last: justify;'> 执行结果</span><span style='width:14px;text-align:left;display:block;'>:</span></p>",  # noqa E501unsafe_allow_html=True)if 'text' in action.result:st.markdown("<p style='text-align: left;'>" + action.result['text'] +'</p>',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 sidebarif '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 changedif '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 sidebarif uploaded_file:file_bytes = uploaded_file.read()file_type = uploaded_file.typeif '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 pathfile_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()

Demo 运行

服务器端输入

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

本地同样采用ssh端口访问
服务器端输入
streamlit run /root/code/lagent/examples/react_web_demo.py --server.address 127.0.0.1 --server.port 6006

浦语·灵笔图文理解创作 Demo

环境准备

需要重新开一个服务器A100(1/4)*2
激活虚拟环境

/root/share/install_conda_env_internlm_base.sh xcomposer-demo

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

下载模型

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

下载代码

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

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