书生·浦语大模型趣味 Demo | shmaur

studio-conda -o internlm-base -t demo

conda activate demo # 进入到环境中
# 安装下面的环境
pip install huggingface-hub==0.17.3
pip install transformers==4.34 
pip install psutil==5.9.8
pip install accelerate==0.24.1
pip install streamlit==1.32.2 
pip install matplotlib==3.8.3 
pip install modelscope==1.9.5
pip install sentencepiece==0.1.99

mkdir -p /root/demo     # 创建路径
touch /root/demo/cli_demo.py  # 创建文件
touch /root/demo/download_mini.py  #创建文件
cd /root/demo #进入到文件目录

import os
from modelscope.hub.snapshot_download import snapshot_download

# 创建保存模型目录
os.system("mkdir /root/models")

# save_dir是模型保存到本地的目录
save_dir="/root/models"

snapshot_download("Shanghai_AI_Laboratory/internlm2-chat-1_8b", 
                  cache_dir=save_dir, 
                  revision='v1.1.0')

python /root/demo/download_mini.py  # 下载模型参数文件

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM


model_name_or_path = "/root/models/Shanghai_AI_Laboratory/internlm2-chat-1_8b"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, device_map='cuda:0')
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='cuda:0')
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("\nUser  >>> ")
    input_text = input_text.replace(' ', '')
    if input_text == "exit":
        break

    length = 0
    for response, _ in model.stream_chat(tokenizer, input_text, messages):
        if response is not None:
            print(response[length:], flush=True, end="")
            length = len(response)

conda activate demo
python /root/demo/cli_demo.py

conda activate demo # 运行环境
cd /root/  # 进入目录
git clone https://gitee.com/InternLM/Tutorial -b camp2  # 下载文件
cd /root/Tutorial # 进入目录
python /root/Tutorial/helloworld/bajie_download.py  # 下载Chat-八戒模型
streamlit run /root/Tutorial/helloworld/bajie_chat.py --server.address 127.0.0.1 --server.port 6006 # 下载完成后运行模型

cd /root/demo/lagent # 进入目录
ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm2-chat-7b /root/models/internlm2-chat-7b # 添加软连接
streamlit run /root/demo/lagent/examples/internlm2_agent_web_demo_hf.py --server.address 127.0.0.1 --server.port 6006 # 运行模型

conda activate demo  # 进入环境
# 补充环境包
pip install timm==0.4.12 sentencepiece==0.1.99 markdown2==2.4.10 xlsxwriter==3.1.2 gradio==4.13.0 modelscope==1.9.5
cd /root/demo   # 进入目录
git clone https://gitee.com/internlm/InternLM-XComposer.git # 下载XComposer库

cd /root/demo/InternLM-XComposer # 进入目录
git checkout f31220eddca2cf6246ee2ddf8e375a40457ff626
# 添加软连接
ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm-xcomposer2-7b /root/models/internlm-xcomposer2-7b
ln -s /root/share/new_models/Shanghai_AI_Laboratory/internlm-xcomposer2-vl-7b /root/models/internlm-xcomposer2-vl-7b
# 启动 InternLM-XComposer
cd /root/demo/InternLM-XComposer
python /root/demo/InternLM-XComposer/examples/gradio_demo_composition.py  \
--code_path /root/models/internlm-xcomposer2-7b \
--private \
--num_gpus 1 \
--port 6006

conda activate demo  # 进入环境

#进入并启动模型
cd /root/demo/InternLM-XComposer 
python /root/demo/InternLM-XComposer/examples/gradio_demo_chat.py  \
--code_path /root/models/internlm-xcomposer2-vl-7b \
--private \
--num_gpus 1 \
--port 6006