本篇介绍书生·浦语大模型的使用，包括智能对话、智能体工具调用和图文理解创作等。

InternLM-Chat-7B 智能对话

环境配置

!conda create --name internlm-chat --clone=/root/share/conda_envs/internlm-base
!/root/.conda/envs/internlm-chat/bin/python -m pip install ipykernel ipywidgets
!/root/.conda/envs/internlm-chat/bin/python -m ipykernel install --user --name internlm-chat --display-name internlm-chat

# restart kernel and use internlm-chat env

1 2	%pip install -q --upgrade pip %pip install -q modelscope==1.9.5 transformers==4.35.2 streamlit==1.24.0 sentencepiece==0.1.99 accelerate==0.24.1

1 2	# 查看当前依赖包信息 %pip list

Package                   Version
------------------------- ------------
accelerate                0.24.1
addict                    2.4.0
aiohttp                   3.9.1
aiosignal                 1.3.1
aliyun-python-sdk-core    2.14.0
aliyun-python-sdk-kms     2.16.2
altair                    5.2.0
asttokens                 2.4.1
async-timeout             4.0.3
attrs                     23.2.0
blinker                   1.7.0
Brotli                    1.0.9
cachetools                5.3.2
certifi                   2023.11.17
cffi                      1.16.0
charset-normalizer        2.0.4
click                     8.1.7
comm                      0.2.1
crcmod                    1.7
cryptography              41.0.3
datasets                  2.13.0
debugpy                   1.8.0
decorator                 5.1.1
dill                      0.3.6
einops                    0.7.0
exceptiongroup            1.2.0
executing                 2.0.1
filelock                  3.13.1
frozenlist                1.4.1
fsspec                    2023.12.2
gast                      0.5.4
gitdb                     4.0.11
GitPython                 3.1.40
gmpy2                     2.1.2
huggingface-hub           0.20.2
idna                      3.4
importlib-metadata        6.11.0
ipykernel                 6.28.0
ipython                   8.19.0
ipywidgets                8.1.1
jedi                      0.19.1
Jinja2                    3.1.2
jmespath                  0.10.0
jsonschema                4.20.0
jsonschema-specifications 2023.12.1
jupyter_client            8.6.0
jupyter_core              5.7.0
jupyterlab-widgets        3.0.9
markdown-it-py            3.0.0
MarkupSafe                2.1.1
matplotlib-inline         0.1.6
mdurl                     0.1.2
mkl-fft                   1.3.8
mkl-random                1.2.4
mkl-service               2.4.0
modelscope                1.9.5
mpmath                    1.3.0
multidict                 6.0.4
multiprocess              0.70.14
nest-asyncio              1.5.8
networkx                  3.1
numpy                     1.26.2
oss2                      2.18.4
packaging                 23.2
pandas                    2.1.4
parso                     0.8.3
pexpect                   4.9.0
Pillow                    9.5.0
pip                       23.3.2
platformdirs              4.1.0
prompt-toolkit            3.0.43
protobuf                  4.25.1
psutil                    5.9.7
ptyprocess                0.7.0
pure-eval                 0.2.2
pyarrow                   14.0.2
pycparser                 2.21
pycryptodome              3.19.1
pydeck                    0.8.1b0
Pygments                  2.17.2
Pympler                   1.0.1
pyOpenSSL                 23.2.0
PySocks                   1.7.1
python-dateutil           2.8.2
pytz                      2023.3.post1
pytz-deprecation-shim     0.1.0.post0
PyYAML                    6.0.1
pyzmq                     25.1.2
referencing               0.32.1
regex                     2023.12.25
requests                  2.31.0
rich                      13.7.0
rpds-py                   0.16.2
safetensors               0.4.1
scipy                     1.11.4
sentencepiece             0.1.99
setuptools                68.0.0
simplejson                3.19.2
six                       1.16.0
smmap                     5.0.1
sortedcontainers          2.4.0
stack-data                0.6.3
streamlit                 1.24.0
sympy                     1.11.1
tenacity                  8.2.3
tokenizers                0.15.0
toml                      0.10.2
tomli                     2.0.1
toolz                     0.12.0
torch                     2.0.1
torchaudio                2.0.2
torchvision               0.15.2
tornado                   6.4
tqdm                      4.66.1
traitlets                 5.14.1
transformers              4.35.2
triton                    2.0.0
typing_extensions         4.7.1
tzdata                    2023.4
tzlocal                   4.3.1
urllib3                   1.26.18
validators                0.22.0
watchdog                  3.0.0
wcwidth                   0.2.13
wheel                     0.41.2
widgetsnbextension        4.0.9
xxhash                    3.4.1
yapf                      0.40.2
yarl                      1.9.4
zipp                      3.17.0
Note: you may need to restart the kernel to use updated packages.

下载模型

1 2	%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

1
2
3

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

/root/code
Cloning into 'InternLM'...


/root/.conda/envs/internlm-chat/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]


remote: Enumerating objects: 2604, done.
remote: Counting objects: 100% (592/592), done.
remote: Compressing objects: 100% (264/264), done.
remote: Total 2604 (delta 324), reused 581 (delta 318), pack-reused 2012
Receiving objects: 100% (2604/2604), 4.87 MiB | 793.00 KiB/s, done.
Resolving deltas: 100% (1608/1608), done.

1 2	%cd InternLM !git checkout 3028f07cb79e5b1d7342f4ad8d11efad3fd13d17

/root/code/InternLM
Note: switching to '3028f07cb79e5b1d7342f4ad8d11efad3fd13d17'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 3028f07 fix(readme): update README with original weight download link (#460)

终端运行

# %%writefile /root/code/InternLM/cli_demo.py
# 1. terminal 中运行命令： python /root/code/InternLM/cli_demo.py
# 2. 直接在 notebook 中运行
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()

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.replace(' ', '')
    if input_text == "exit":
        break
    response, history = model.chat(tokenizer, input_text, history=messages)
    messages.append((input_text, response))
    print(f"robot >>> {response}")

Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]


=============Welcome to InternLM chatbot, type 'exit' to exit.=============


User  >>>  你好


robot >>> 你好！有什么我能帮你的吗？


User  >>>  帮我写个 300 字的小故事


robot >>> 有一天，一个年轻的男孩来到了一个神秘的森林。他不知道这个森林里到底有什么，但是他知道，他必须进去。他跟着森林里的指引走了一段路，最终来到了一座古老的神庙。

当男孩走到神庙门前时，他感到一种奇怪的气息。他推开门，走进神庙。他发现里面有一个神秘的书房，里面放着许多古老的书籍和文物。他拿起一本破旧的书籍，翻到第一页，上面写着一个故事。

故事讲述了一个叫做“神灯”的神秘物品。男孩被这个故事吸引了，他开始阅读这本书，希望能够找到这个物品。他读了很长时间，最终理解了这个故事，明白了神灯的含义。

男孩离开了神庙，回到了家中。他开始思考，他是否应该去寻找神灯。他决定去寻找这个神秘的物品，他相信自己可以找到它。

他走了很长一段路，遇到了许多挑战和困难，但是他始终没有放弃。最终，他找到了神灯。他拿起这个神秘物品，开始使用它。

他发现，神灯可以帮助他实现他的梦想。他开始使用神灯，他能够做任何事情。他能够变成任何他想要的形状，他能够变得非常有力量。

然而，男孩开始意识到，神灯并不是一个简单的物品。他开始发现，他必须付出一些代价才能使用神灯。他必须做很多事情，才能够使用它。他开始思考，他是否愿意付出这个代价。

最终，男孩决定使用神灯，他开始使用它来帮助那些需要帮助的人。他开始变得非常有名，人们开始称呼他为“神灯使者”。

男孩意识到，他必须继续使用神灯，但是他必须小心使用它。他不能让神灯的力量控制他，他必须控制它。最终，男孩学会了如何使用神灯，他成为了一个真正的神灯使者。

男孩回到了神庙，他感谢神庙，感谢神灯，感谢那个神秘的物品。他知道，他必须继续使用神灯，但是必须小心使用它，不能让它控制他。

男孩回到了家中，他开始使用神灯来帮助那些需要帮助的人。他成为了一个真正的神灯使者，他的故事被传颂了下去，成为了一段神话。


User  >>>  exit

WEB 端运行

%%writefile /root/code/InternLM/web_demo_user.py
"""
This script refers to the dialogue example of streamlit, the interactive generation code of chatglm2 and transformers.
We mainly modified part of the code logic to adapt to the generation of our model.
Please refer to these links below for more information:
    1. streamlit chat example: https://docs.streamlit.io/knowledge-base/tutorials/build-conversational-apps
    2. chatglm2: https://github.com/THUDM/ChatGLM2-6B
    3. transformers: https://github.com/huggingface/transformers
"""

from dataclasses import asdict

import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import logging

from tools.transformers.interface import GenerationConfig, generate_interactive

logger = logging.get_logger(__name__)


def on_btn_click():
    del st.session_state.messages


@st.cache_resource
def load_model():
    model = (
        AutoModelForCausalLM.from_pretrained("/root/model/Shanghai_AI_Laboratory/internlm-chat-7b", trust_remote_code=True)
        .to(torch.bfloat16)
        .cuda()
    )
    tokenizer = AutoTokenizer.from_pretrained("/root/model/Shanghai_AI_Laboratory/internlm-chat-7b", trust_remote_code=True)
    return model, tokenizer


def prepare_generation_config():
    with st.sidebar:
        max_length = st.slider("Max Length", min_value=32, max_value=2048, value=2048)
        top_p = st.slider("Top P", 0.0, 1.0, 0.8, step=0.01)
        temperature = st.slider("Temperature", 0.0, 1.0, 0.7, step=0.01)
        st.button("Clear Chat History", on_click=on_btn_click)

    generation_config = GenerationConfig(max_length=max_length, top_p=top_p, temperature=temperature)

    return generation_config


user_prompt = "<|User|>:{user}\n"
robot_prompt = "<|Bot|>:{robot}<eoa>\n"
cur_query_prompt = "<|User|>:{user}<eoh>\n<|Bot|>:"


def combine_history(prompt):
    messages = st.session_state.messages
    total_prompt = ""
    for message in messages:
        cur_content = message["content"]
        if message["role"] == "user":
            cur_prompt = user_prompt.replace("{user}", cur_content)
        elif message["role"] == "robot":
            cur_prompt = robot_prompt.replace("{robot}", cur_content)
        else:
            raise RuntimeError
        total_prompt += cur_prompt
    total_prompt = total_prompt + cur_query_prompt.replace("{user}", prompt)
    return total_prompt


def main():
    # torch.cuda.empty_cache()
    print("load model begin.")
    model, tokenizer = load_model()
    print("load model end.")

    user_avator = "doc/imgs/user.png"
    robot_avator = "doc/imgs/robot.png"

    st.title("InternLM-Chat-7B")

    generation_config = prepare_generation_config()

    # Initialize chat history
    if "messages" not in st.session_state:
        st.session_state.messages = []

    # Display chat messages from history on app rerun
    for message in st.session_state.messages:
        with st.chat_message(message["role"], avatar=message.get("avatar")):
            st.markdown(message["content"])

    # Accept user input
    if prompt := st.chat_input("What is up?"):
        # Display user message in chat message container
        with st.chat_message("user", avatar=user_avator):
            st.markdown(prompt)
        real_prompt = combine_history(prompt)
        # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt, "avatar": user_avator})

        with st.chat_message("robot", avatar=robot_avator):
            message_placeholder = st.empty()
            for cur_response in generate_interactive(
                model=model,
                tokenizer=tokenizer,
                prompt=real_prompt,
                additional_eos_token_id=103028,
                **asdict(generation_config),
            ):
                # Display robot response in chat message container
                message_placeholder.markdown(cur_response + "▌")
            message_placeholder.markdown(cur_response)
        # Add robot response to chat history
        st.session_state.messages.append({"role": "robot", "content": cur_response, "avatar": robot_avator})
        torch.cuda.empty_cache()


if __name__ == "__main__":
    main()

Writing /root/code/InternLM/web_demo_user.py

1	import os, sys

1 2	%cd /root/code/InternLM/ !HF_ENDPOINT=https://hf-mirror.com {os.path.join(sys.exec_prefix, 'bin/streamlit')} run web_demo.py --server.address 127.0.0.1 --server.port 6006

/root/code/InternLM


/root/.conda/envs/internlm-chat/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]



Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.

You can now view your Streamlit app in your browser.

URL: http://127.0.0.1:6006

load model begin.
Loading checkpoint shards: 100%|██████████████████| 8/8 [00:25<00:00,  3.20s/it]
/usr/local/lib/python3.8/dist-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 0.00 MB. The target location /root/.cache/huggingface/hub only has 0.00 MB free disk space.
  warnings.warn(
/usr/local/lib/python3.8/dist-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 0.00 MB. The target location /root/.cache/huggingface/hub/models--internlm--internlm-chat-7b/blobs only has 0.00 MB free disk space.
  warnings.warn(
tokenizer_config.json: 343B [00:00, 31.9kB/s]
/usr/local/lib/python3.8/dist-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 0.01 MB. The target location /root/.cache/huggingface/hub only has 0.00 MB free disk space.
  warnings.warn(
/usr/local/lib/python3.8/dist-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 0.01 MB. The target location /root/.cache/huggingface/hub/models--internlm--internlm-chat-7b/blobs only has 0.00 MB free disk space.
  warnings.warn(
tokenization_internlm.py: 8.95kB [00:00, 35.6MB/s]
A new version of the following files was downloaded from https://huggingface.co/internlm/internlm-chat-7b:
- tokenization_internlm.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
/usr/local/lib/python3.8/dist-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 1.66 MB. The target location /root/.cache/huggingface/hub only has 0.00 MB free disk space.
  warnings.warn(
/usr/local/lib/python3.8/dist-packages/huggingface_hub/file_download.py:983: UserWarning: Not enough free disk space to download the file. The expected file size is: 1.66 MB. The target location /root/.cache/huggingface/hub/models--internlm--internlm-chat-7b/blobs only has 0.00 MB free disk space.
  warnings.warn(
tokenizer.model: 100%|█████████████████████| 1.66M/1.66M [00:00<00:00, 2.92MB/s]
special_tokens_map.json: 95.0B [00:00, 18.2kB/s]
load model end.
load model begin.
load model end.
load model begin.
load model end.
^C
Stopping...

上面运行成功后，需要利用SSH把远程服务器的6006端口映射到本地的某个端口（这里设为6006），这里的SSH端口根据开发机创建时动态开启的端口的不同而不同，我这里是 33449

1 2	# 本地机器运行，需要把本地的公钥添加到个人服务器账号上 ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 33449

Lagent 智能体工具调用

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，并提供了一些典型工具为大语言模型赋能。通过 Lagent 框架可以更好的发挥 InternLM 的全部性能。

环境准备

同上一个 InternLM-Chat-7B 智能对话的环境

下载模型

同上一个 InternLM-Chat-7B 智能对话的模型

Lagent 安装

1 2	%cd /root/code !git clone https://gitee.com/internlm/lagent.git

/root/code
Cloning into 'lagent'...
remote: Enumerating objects: 414, done.
remote: Counting objects: 100% (414/414), done.
remote: Compressing objects: 100% (188/188), done.
remote: Total 414 (delta 197), reused 414 (delta 197), pack-reused 0
Receiving objects: 100% (414/414), 214.97 KiB | 306.00 KiB/s, done.
Resolving deltas: 100% (197/197), done.

1
2
3

%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

/root/code/lagent
Note: switching to '511b03889010c4811b1701abb153e02b8e94fb5e'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 511b038 update header-logo (#72)

推理

%%writefile /root/code/lagent/examples/react_web_demo_user.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(
                "<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 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(
                "<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
                unsafe_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 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()

Writing /root/code/lagent/examples/react_web_demo_user.py

1	import sys, os

1	!HF_ENDPOINT=https://hf-mirror.com {os.path.join(sys.exec_prefix, 'bin/streamlit')} run /root/code/lagent/examples/react_web_demo_user.py --server.address 127.0.0.1 --server.port 6006

Collecting usage statistics. To deactivate, set browser.gatherUsageStats to False.


You can now view your Streamlit app in your browser.

URL: http://127.0.0.1:6006


  A new version of Streamlit is available.

  See what's new at https://discuss.streamlit.io/c/announcements

  Enter the following command to upgrade:
  $pip install streamlit --upgrade

Loading checkpoint shards: 100%|██████████████████| 8/8 [00:14<00:00,  1.83s/it]
2024-01-06 15:44:48.261 [{'role': 'user', 'content': '你好'}, {'role': 'assistant', 'content': 'Thought: 你好，有什么我可以帮助你的吗？\nAction: PythonInterpreter\nAction Input: def solution():\n    answer = "你好"\n    return answer'}, {'role': 'system', 'content': 'Response:你好\n'}, {'role': 'assistant', 'content': 'Thought: 我已经收到了你的消息，你需要我做什么？\nAction: PythonInterpreter\nAction Input: def solution():\n    answer = "你好"\n    return answer'}, {'role': 'system', 'content': 'Response:你好\n'}, {'role': 'assistant', 'content': 'Thought: 我已经收到了你的消息，但是你的消息中没有提供任何需要我帮助的问题或指令。请重新发送你的问题或指令，我会尽力帮助你。\nAction: PythonInterpreter\nAction Input: def solution():\n    answer = "你好"\n    return answer'}, {'role': 'system', 'content': 'Response:你好\n'}, {'role': 'assistant', 'content': 'Thought: 我已经收到了你的消息，但是你的消息中没有提供任何需要我帮助的问题或指令。请重新发送你的问题或指令，我会尽力帮助你。\nAction: PythonInterpreter\nAction Input: def solution():\n    answer = "你好"\n    return answer'}, {'role': 'system', 'content': 'Response:你好\n'}]
2024-01-06 15:45:52.619 [{'role': 'user', 'content': '帮我求解：4x + 3y = 7; x + y = 2，x 和 y 分别等于多少'}, {'role': 'assistant', 'content': 'Thought: 这是一道方程求解题，需要用到求解器Solver来求解这个方程组。\nAction: PythonInterpreter\nAction Input: def solution():\n\tfrom sympy import symbols, Eq, solve, E, E\n\tx = symbols("x", real=True)\n\ty = symbols("y", real=True)\n\tresult = solve([Eq(4*x + 3*y, 7), Eq(x + y, 2)], [x, y], dict=True)\n\treturn result'}, {'role': 'system', 'content': 'Response:[{x: 1, y: 1}]\n'}, {'role': 'assistant', 'content': 'Thought: Base on the result of the code, the answer is:\nFinal Answer: 根据求解器的计算结果，x 的值为 1，y 的值为 1。'}]
^C
 Stopping...

1 2	# 本地机器运行，需要把本地的公钥添加到个人服务器账号上 ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 33449

浦语·灵笔图文理解创作

internlm-xcomposer-7b 模型部署一个图文理解创作

环境准备

1
2

# 基本环境使用与 第一部分相同的，另外，需要安装特定的几个依赖包
%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

模型下载

1 2	%mkdir -p /root/model/Shanghai_AI_Laboratory %cp -r /root/share/temp/model_repos/internlm-xcomposer-7b /root/model/Shanghai_AI_Laboratory

推理

%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

/root/code
Cloning into 'InternLM-XComposer'...


/root/.conda/envs/internlm-chat/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]


remote: Enumerating objects: 680, done.
remote: Counting objects: 100% (680/680), done.
remote: Compressing objects: 100% (273/273), done.
remote: Total 680 (delta 361), reused 680 (delta 361), pack-reused 0
Receiving objects: 100% (680/680), 10.74 MiB | 8.78 MiB/s, done.
Resolving deltas: 100% (361/361), done.
/root/code/InternLM-XComposer
Note: switching to '3e8c79051a1356b9c388a6447867355c0634932d'.

You are in 'detached HEAD' state. You can look around, make experimental
changes and commit them, and you can discard any commits you make in this
state without impacting any branches by switching back to a branch.

If you want to create a new branch to retain commits you create, you may
do so (now or later) by using -c with the switch command. Example:

  git switch -c <new-branch-name>

Or undo this operation with:

  git switch -

Turn off this advice by setting config variable advice.detachedHead to false

HEAD is now at 3e8c790 add polar in readme

1	import sys

%cd /root/code/InternLM-XComposer
!{sys.executable} examples/web_demo.py  \
    --folder /root/model/Shanghai_AI_Laboratory/internlm-xcomposer-7b \
    --num_gpus 1 \
    --port 6006

/root/code/InternLM-XComposer


/root/.conda/envs/internlm-chat/lib/python3.10/site-packages/IPython/core/magics/osm.py:417: UserWarning: using dhist requires you to install the `pickleshare` library.
  self.shell.db['dhist'] = compress_dhist(dhist)[-100:]


Init VIT ... Done
Init Perceive Sampler ... Done
Init InternLM ... Done
Loading checkpoint shards: 100%|██████████████████| 4/4 [00:25<00:00,  6.37s/it]
 load model done:  <class 'transformers_modules.internlm-xcomposer-7b.modeling_InternLM_XComposer.InternLMXComposerForCausalLM'>
/root/code/InternLM-XComposer/examples/web_demo.py:1068: GradioDeprecationWarning: The `style` method is deprecated. Please set these arguments in the constructor instead.
  chat_textbox = gr.Textbox(
Running on local URL:  http://0.0.0.0:6006
init

Could not create share link. Missing file: /root/.conda/envs/internlm-chat/lib/python3.10/site-packages/gradio/frpc_linux_amd64_v0.2. 

Please check your internet connection. This can happen if your antivirus software blocks the download of this file. You can install manually by following these steps: 

1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64
2. Rename the downloaded file to: frpc_linux_amd64_v0.2
3. Move the file to this location: /root/.conda/envs/internlm-chat/lib/python3.10/site-packages/gradio
<object object at 0x7efdf06ec340>
郁金香（学名：Tulipa gesneriana L.）是百合科郁金香属植物，又名洋荷花、草麝香等。原产于地中海沿岸以及西亚和南西伯利亚的半干旱或高寒地区。荷兰人最早将郁金香作为观赏花卉；16世纪中叶，郁金香被引入中国；17世纪传入欧洲各国。

  

郁金香花色丰富，有红、橙、黄、紫、白、黑、双色及镶边等多种颜色，而且同一植株上可呈现不同色彩的花朵。花朵硕大艳丽，富丽堂皇，芳香四溢，给人以庄重、华贵、富丽之感。它不仅具有很高的观赏价值，而且还有较高的经济作物品种开发利用价值。

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郁金香（学名：Tulipa gesneriana L.）是百合科郁金香属植物，又名洋荷花、草麝香等。原产于地中海沿岸以及西亚和南西伯利亚的半干旱或高寒地区。荷兰人最早将郁金香作为观赏花卉；16世纪中叶，郁金香被引入中国；17世纪传入欧洲各国。
郁金香花色丰富，有红、橙、黄、紫、白、黑、双色及镶边等多种颜色，而且同一植株上可呈现不同色彩的花朵。花朵硕大艳丽，富丽堂皇，芳香四溢，给人以庄重、华贵、富丽之感。它不仅具有很高的观赏价值，而且还有较高的经济作物品种开发利用价值。
<TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1>
适合插入图像的行是<Seg0>, <Seg2>.
[0, 2]
郁金香的花朵，颜色丰富多样。
郁金香的花朵，花朵硕大艳丽。
{0: '郁金香的花朵，颜色丰富多样。', 2: '郁金香的花朵，花朵硕大艳丽。'}
{0: '郁金香的花朵，颜色丰富多样。', 2: '郁金香的花朵，花朵硕大艳丽。'}
https://static.openxlab.org.cn/lingbi/jpg-images/105d05c6bc63e3f446c715f10b1c5bb349e09c1e2860fa2d510d0aabde193a1a.jpg
download image with url
image downloaded
https://static.openxlab.org.cn/lingbi/jpg-images/11eba488365ed9c830601ab473788c82b6fda279d05c2485227ee8cb089b2f51.jpg
download image with url
image downloaded
model_select_image
0 郁金香（学名：Tulipa gesneriana L.）是百合科郁金香属植物，又名洋荷花、草麝香等。原产于地中海沿岸以及西亚和南西伯利亚的半干旱或高寒地区。荷兰人最早将郁金香作为观赏花卉；16世纪中叶，郁金香被引入中国；17世纪传入欧洲各国。
<div align="center"> <img src="file=articles/如何培育郁金香/temp_1000_0.png" width = 500/> </div>
1 郁金香花色丰富，有红、橙、黄、紫、白、黑、双色及镶边等多种颜色，而且同一植株上可呈现不同色彩的花朵。花朵硕大艳丽，富丽堂皇，芳香四溢，给人以庄重、华贵、富丽之感。它不仅具有很高的观赏价值，而且还有较高的经济作物品种开发利用价值。
2 <TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1><TOKENS_UNUSED_1>
<div align="center"> <img src="file=articles/如何培育郁金香/temp_1002_2.png" width = 500/> </div>
^C
Keyboard interruption in main thread... closing server.

上面运行成功后，需要利用SSH把远程服务器的6006端口映射到本地的某个端口（这里设为6006），这里的SSH端口根据开发机创建时动态开启的端口的不同而不同，我这里是 34000

1 2	# 本地机器运行，需要把本地的公钥添加到个人服务器账号上 ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 34000

从 hugging face 下载 InternLM-20b 的 config.json

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）。

1	%pip install -U huggingface_hub

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: huggingface_hub in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (0.20.2)
Requirement already satisfied: filelock in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (3.13.1)
Requirement already satisfied: fsspec>=2023.5.0 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (2023.12.2)
Requirement already satisfied: requests in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (2.31.0)
Requirement already satisfied: tqdm>=4.42.1 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (4.66.1)
Requirement already satisfied: pyyaml>=5.1 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (6.0.1)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (4.9.0)
Requirement already satisfied: packaging>=20.9 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from huggingface_hub) (23.2)
Requirement already satisfied: charset-normalizer<4,>=2 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from requests->huggingface_hub) (2.0.4)
Requirement already satisfied: idna<4,>=2.5 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from requests->huggingface_hub) (3.4)
Requirement already satisfied: urllib3<3,>=1.21.1 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from requests->huggingface_hub) (1.26.18)
Requirement already satisfied: certifi>=2017.4.17 in /root/.conda/envs/internlm-chat/lib/python3.10/site-packages (from requests->huggingface_hub) (2023.11.17)
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
Note: you may need to restart the kernel to use updated packages.

1	import sys, os

1	!HF_ENDPOINT=https://hf-mirror.com {os.path.join(sys.exec_prefix, 'bin/huggingface-cli')} download --resume-download internlm/internlm-20b config.json --local-dir .

Consider using `hf_transfer` for faster downloads. This solution comes with some limitations. See https://huggingface.co/docs/huggingface_hub/hf_transfer for more details.
./config.json

1	import json

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with open('./config.json', 'r') as jf:
    config = json.load(jf)
config

{'architectures': ['InternLMForCausalLM'],
 'auto_map': {'AutoConfig': 'configuration_internlm.InternLMConfig',
  'AutoModel': 'modeling_internlm.InternLMForCausalLM',
  'AutoModelForCausalLM': 'modeling_internlm.InternLMForCausalLM'},
 'bias': False,
 'bos_token_id': 1,
 'eos_token_id': 2,
 'hidden_act': 'silu',
 'hidden_size': 5120,
 'initializer_range': 0.02,
 'intermediate_size': 13824,
 'max_position_embeddings': 4096,
 'model_type': 'internlm',
 'num_attention_heads': 40,
 'num_hidden_layers': 60,
 'num_key_value_heads': 40,
 'pad_token_id': 2,
 'pretraining_tp': 1,
 'rms_norm_eps': 1e-06,
 'rope_scaling': None,
 'rope_theta': 10000.0,
 'tie_word_embeddings': False,
 'torch_dtype': 'bfloat16',
 'transformers_version': '4.33.1',
 'use_cache': True,
 'vocab_size': 103168,
 'rotary': {'base': 10000, 'type': 'dynamic'}}