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https://github.com/datawhalechina/self-llm.git
synced 2026-09-19 01:36:47 +08:00
completed interlm
This commit is contained in:
KMnO4-zx
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# Lagent+InternLM-Chat-7B-V1.1
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## 环境准备
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选择和第一个 `InternLM` 一样的 `AutoDL` 镜像环境，运行以下命令安装依赖，如果上一个 `InternLM-Chat-7B` 已经配置好环境不需要重复安装.
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```shell
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# 更换 pypi 源加速库的安装
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pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
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pip install modelscope
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pip install transformers
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pip install streamlit==1.24.0
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pip install sentencepiece
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pip install accelerate
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```
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## 模型下载
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在 `/root/autodl-tmp` 路径下新建 `download.py` 文件并在其中输入以下内容，并运行 `python /root/autodl-tmp/download.py`执行下载，模型大小为 14 GB，下载模型大概需要 10~20 分钟
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```python
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import torch
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from modelscope import snapshot_download, AutoModel, AutoTokenizer
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import os
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model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-chat-7b-v1_1', cache_dir='/root/autodl-tmp', revision='master')
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```
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## Lagent 安装
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首先我们运行以下命令打开学术加速 `clone` `lagent`仓库，并通过 `pip install -e .`源码安装 `Lagent`
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```shell
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source /etc/network_turbo
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cd /root/autodl-tmp
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git clone https://github.com/InternLM/lagent.git
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git checkout 511b03889010c4811b1701abb153e02b8e94fb5e # 尽量保证和教程commit版本一致
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cd lagent
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pip install -e . # 源码安装
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unset http_proxy && unset https_proxy
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```
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## 修改代码
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由于代码修改的地方比较多，大家直接将`/root/autodl-tmp/lagent/examples/react_web_demo.py` 内容替换为以下代码
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```python
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import copy
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import os
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import streamlit as st
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from streamlit.logger import get_logger
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from lagent.actions import ActionExecutor, GoogleSearch, PythonInterpreter
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from lagent.agents.react import ReAct
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from lagent.llms import GPTAPI
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from lagent.llms.huggingface import HFTransformerCasualLM
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class SessionState:
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def init_state(self):
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"""Initialize session state variables."""
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st.session_state['assistant'] = []
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st.session_state['user'] = []
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#action_list = [PythonInterpreter(), GoogleSearch()]
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action_list = [PythonInterpreter()]
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st.session_state['plugin_map'] = {
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action.name: action
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for action in action_list
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}
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st.session_state['model_map'] = {}
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st.session_state['model_selected'] = None
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st.session_state['plugin_actions'] = set()
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def clear_state(self):
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"""Clear the existing session state."""
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st.session_state['assistant'] = []
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st.session_state['user'] = []
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st.session_state['model_selected'] = None
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if 'chatbot' in st.session_state:
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st.session_state['chatbot']._session_history = []
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class StreamlitUI:
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def __init__(self, session_state: SessionState):
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self.init_streamlit()
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self.session_state = session_state
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def init_streamlit(self):
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"""Initialize Streamlit's UI settings."""
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st.set_page_config(
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layout='wide',
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page_title='lagent-web',
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page_icon='./docs/imgs/lagent_icon.png')
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# st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
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st.sidebar.title('模型控制')
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def setup_sidebar(self):
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"""Setup the sidebar for model and plugin selection."""
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model_name = st.sidebar.selectbox(
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'模型选择：', options=['gpt-3.5-turbo','internlm'])
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if model_name != st.session_state['model_selected']:
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model = self.init_model(model_name)
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self.session_state.clear_state()
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st.session_state['model_selected'] = model_name
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if 'chatbot' in st.session_state:
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del st.session_state['chatbot']
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else:
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model = st.session_state['model_map'][model_name]
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plugin_name = st.sidebar.multiselect(
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'插件选择',
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options=list(st.session_state['plugin_map'].keys()),
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default=[list(st.session_state['plugin_map'].keys())[0]],
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)
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plugin_action = [
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st.session_state['plugin_map'][name] for name in plugin_name
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]
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if 'chatbot' in st.session_state:
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st.session_state['chatbot']._action_executor = ActionExecutor(
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actions=plugin_action)
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if st.sidebar.button('清空对话', key='clear'):
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self.session_state.clear_state()
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uploaded_file = st.sidebar.file_uploader(
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'上传文件', type=['png', 'jpg', 'jpeg', 'mp4', 'mp3', 'wav'])
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return model_name, model, plugin_action, uploaded_file
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def init_model(self, option):
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"""Initialize the model based on the selected option."""
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if option not in st.session_state['model_map']:
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if option.startswith('gpt'):
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st.session_state['model_map'][option] = GPTAPI(
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model_type=option)
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else:
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st.session_state['model_map'][option] = HFTransformerCasualLM(
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'/root/autodl-tmp/Shanghai_AI_Laboratory/internlm-chat-7b-v1_1')
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return st.session_state['model_map'][option]
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def initialize_chatbot(self, model, plugin_action):
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"""Initialize the chatbot with the given model and plugin actions."""
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return ReAct(
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llm=model, action_executor=ActionExecutor(actions=plugin_action))
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def render_user(self, prompt: str):
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with st.chat_message('user'):
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st.markdown(prompt)
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def render_assistant(self, agent_return):
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with st.chat_message('assistant'):
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for action in agent_return.actions:
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if (action):
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self.render_action(action)
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st.markdown(agent_return.response)
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def render_action(self, action):
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with st.expander(action.type, expanded=True):
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st.markdown(
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"<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
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+ action.type + '</span></p>',
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unsafe_allow_html=True)
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st.markdown(
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"<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
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+ action.thought + '</span></p>',
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unsafe_allow_html=True)
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if (isinstance(action.args, dict) and 'text' in action.args):
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st.markdown(
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"<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 E501
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unsafe_allow_html=True)
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st.markdown(action.args['text'])
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self.render_action_results(action)
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def render_action_results(self, action):
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"""Render the results of action, including text, images, videos, and
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audios."""
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if (isinstance(action.result, dict)):
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st.markdown(
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"<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 E501
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unsafe_allow_html=True)
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if 'text' in action.result:
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st.markdown(
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"<p style='text-align: left;'>" + action.result['text'] +
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'</p>',
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unsafe_allow_html=True)
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if 'image' in action.result:
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image_path = action.result['image']
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image_data = open(image_path, 'rb').read()
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st.image(image_data, caption='Generated Image')
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if 'video' in action.result:
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video_data = action.result['video']
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video_data = open(video_data, 'rb').read()
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st.video(video_data)
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if 'audio' in action.result:
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audio_data = action.result['audio']
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audio_data = open(audio_data, 'rb').read()
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st.audio(audio_data)
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def main():
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logger = get_logger(__name__)
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# Initialize Streamlit UI and setup sidebar
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if 'ui' not in st.session_state:
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session_state = SessionState()
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session_state.init_state()
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st.session_state['ui'] = StreamlitUI(session_state)
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else:
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st.set_page_config(
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layout='wide',
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page_title='lagent-web',
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page_icon='./docs/imgs/lagent_icon.png')
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# st.header(':robot_face: :blue[Lagent] Web Demo ', divider='rainbow')
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model_name, model, plugin_action, uploaded_file = st.session_state[
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'ui'].setup_sidebar()
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# Initialize chatbot if it is not already initialized
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# or if the model has changed
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if 'chatbot' not in st.session_state or model != st.session_state[
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'chatbot']._llm:
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st.session_state['chatbot'] = st.session_state[
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'ui'].initialize_chatbot(model, plugin_action)
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for prompt, agent_return in zip(st.session_state['user'],
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st.session_state['assistant']):
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st.session_state['ui'].render_user(prompt)
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st.session_state['ui'].render_assistant(agent_return)
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# User input form at the bottom (this part will be at the bottom)
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# with st.form(key='my_form', clear_on_submit=True):
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if user_input := st.chat_input(''):
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st.session_state['ui'].render_user(user_input)
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st.session_state['user'].append(user_input)
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# Add file uploader to sidebar
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if uploaded_file:
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file_bytes = uploaded_file.read()
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file_type = uploaded_file.type
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if 'image' in file_type:
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st.image(file_bytes, caption='Uploaded Image')
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elif 'video' in file_type:
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st.video(file_bytes, caption='Uploaded Video')
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elif 'audio' in file_type:
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st.audio(file_bytes, caption='Uploaded Audio')
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# Save the file to a temporary location and get the path
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file_path = os.path.join(root_dir, uploaded_file.name)
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with open(file_path, 'wb') as tmpfile:
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tmpfile.write(file_bytes)
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st.write(f'File saved at: {file_path}')
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user_input = '我上传了一个图像，路径为: {file_path}. {user_input}'.format(
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file_path=file_path, user_input=user_input)
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agent_return = st.session_state['chatbot'].chat(user_input)
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st.session_state['assistant'].append(copy.deepcopy(agent_return))
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logger.info(agent_return.inner_steps)
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st.session_state['ui'].render_assistant(agent_return)
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if __name__ == '__main__':
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root_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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root_dir = os.path.join(root_dir, 'tmp_dir')
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os.makedirs(root_dir, exist_ok=True)
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main()
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```
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## Demo 运行
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```shell
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streamlit run /root/autodl-tmp/lagent/examples/react_web_demo.py --server.address 127.0.0.1 --server.port 6006
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```
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用同样的方法将端口映射到本地，运行成功后，我们在 `Web` 页面选择 `InternLM` 模型，等待模型加载完毕后，输入数学问题 已知 `2x+3=10`，求`x` ,此时 `InternLM-Chat-7B-V1.1` 模型理解题意生成解此题的 `Python`代码，`Lagent` 调度送入 `Python` 代码解释器求出该问题的解。
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# 浦语灵笔图文理解&创作
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## 环境准备
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首先在 `AutoDL` 上租一台显卡驱动支持 `11.7` 以上的双卡 `3090` 机器.
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在选择镜像是选择 `Miniconda` --> `conda3` --> `3.8(ubuntu20.04)`--> `11.6`
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打开 `jupyter lab` 中的终端，首先运行以下命令安装 `PyTorch 2.0.1`
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```shell
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pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu117
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```
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接下来运行以下命令，安装 `transformers`、`gradio` 等依赖包。请严格安装以下版本安装！
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```shell
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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
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```
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## 模型下载
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安装`modelscope`，下载模型的老朋友了
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```shell
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pip install modelscope
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```
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在 `/root/autodl-tmp` 路径下新建 `download.py` 文件并在其中输入以下内容，并运行 `python /root/autodl-tmp/download.py`执行下载
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```python
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import torch
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from modelscope import snapshot_download, AutoModel, AutoTokenizer
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import os
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model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-xcomposer-7b', cache_dir='/root/autodl-tmp', revision='master')
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```
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## 代码准备
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在 `/root/autodl-tmp` `git clone InternLM-XComposer` 仓库的代码
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```shell
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source /etc/network_turbo
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cd /root/autodl-tmp
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git clone https://github.com/InternLM/InternLM-XComposer.git
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git checkout 3e8c79051a1356b9c388a6447867355c0634932d # 最好保证和教程的commit版本一致
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unset http_proxy && unset https_proxy
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```
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## Demo 运行
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在终端运行以下代码：
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```shell
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cd /root/autodl-tmp/InternLM-XComposer
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python examples/web_demo.py \
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--folder /root/autodl-tmp/Shanghai_AI_Laboratory/internlm-xcomposer-7b \
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--num_gpus 2 \
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--port 6006
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```
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将autodl的端口映射到本地之后，用浏览器代开`http://localhost:6006/`，我们以`又见敦煌`为提示词，体验图文创作的功能，如下图所示：
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接下来，我们可以体验以下图片理解的能力，如下所示~
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Binary file not shown.
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After Width: | Height: | Size: 310 KiB |
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After Width: | Height: | Size: 65 KiB |
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After Width: | Height: | Size: 96 KiB |
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After Width: | Height: | Size: 596 KiB |
@@ -1,14 +1,14 @@
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# llm-QuicklyDeploy
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基于AutoDL快速部署开源大模型，更适合中国宝宝的部署教程
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基于AutoDL快速部署开源大模型，更适合中国宝宝的部署教程。
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# 模型
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- InternLM
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- [x] InternLM-Chat-7B
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- [ ] Lagent+InternLM-Chat-7B-V1.1
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- [ ] 浦语灵笔图文理解&创作
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- [x] Lagent+InternLM-Chat-7B-V1.1
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- [x] 浦语灵笔图文理解&创作
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- ChatGLM
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- [ ] ChatGLM2-6B
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- [ ] ChatGLM3-6B
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- [ ] CogVlm
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- Qwen
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- [ ] Qwen-7B
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