今天，我们来记录如何8GB玩转大模型Demo
环境配置
先创建一个可用的环境
在终端输入如下命令
# 创建环境
conda create -n demo python=3.10 -y
# 激活环境
conda activate demo
# 安装 torch
conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia -y
# 安装其他依赖
pip install transformers==4.38
pip install sentencepiece==0.1.99
pip install einops==0.8.0
pip install protobuf==5.27.2
pip install accelerate==0.33.0
pip install streamlit==1.37.0
Cli Demo 部署 InternLM2-Chat-1.8B 模型
首先，我们创建一个目录，用于存放我们的代码。并创建一个 cli_demo.py
mkdir -p /root/demo
touch /root/demo/cli_demo.py
然后，我们将下面的代码复制到 cli_demo.py 中
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name_or_path = "/root/share/new_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)
效果如图所示
Streamlit Web Demo 部署 InternLM2-Chat-1.8B 模型
接下来，我们演示如何部署它到本地
先部署仓库到本地
cd /root/demo
git clone https://github.com/InternLM/Tutorial.git
然后，我们执行如下代码来启动一个 Streamlit 服务
cd /root/demo
streamlit run /root/demo/Tutorial/tools/streamlit_demo.py --server.address 127.0.0.1 --server.port 6006
接下来，我们在本地的 PowerShell 中输入以下命令，将端口映射到本地
ssh -CNg -L 6006:127.0.0.1:6006 root@ssh.intern-ai.org.cn -p 你的 ssh 端口号
如果准确无误，因有如下输出
在完成端口映射后，我们便可以通过浏览器访问
http://localhost:6006 来启动我们的 Demo
效果如下图所示：
