1、利用vllm可以显著推理加速大模型
conda create -n vllm python=3.10 conda activate vllm conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
2、启动推理 python -m vllm.entrypoints.openai.api_server --tensor-parallel-size=1 --trust-remote-code --max-model-len 1024 --model THUDM/chatglm3-6b 指定ip和端口：--host 127.0.0.1 --port 8101
python -m vllm.entrypoints.openai.api_server --port 8101 --tensor-parallel-size=1 --trust-remote-code --max-model-len 1024 --model THUDM/chatglm3-6b
CUDA_VISIBLE_DEVICES=6,7 python -m vllm.entrypoints.openai.api_server
--model="/data/mnt/ShareFolder/common_models/Ziya-Reader-13B-v1.0"
--max-model-len=8192
--tensor-parallel-size=2
--trust-remote-code
--port=8101
3、测试
curl http:https://127.0.0.1:8101/v1/completions
-H "Content-Type: application/json"
-d '{
"model": "THUDM/chatglm3-6b",
"prompt": "请用20字内回复我,你今年多大了",
"max_tokens": 20,
"temperature": 0
}'
多轮对话
curl -X POST "http:https://127.0.0.1:8101/v1/completions"
-H "Content-Type: application/json"
-d "{"model": "THUDM/chatglm3-6b","prompt": "你叫什么名字", "history": [{"role": "user", "content": "你出生在哪里."}, {"role": "assistant", "content": "出生在北京"}]}"
多轮对话
curl -X POST "http:https://127.0.0.1:8101/v1/chat/completions"
-H "Content-Type: application/json"
-d "{"model": "THUDM/chatglm3-6b", "messages": [{"role": "system", "content": "You are ChatGLM3, a large language model trained by Zhipu.AI. Follow the user's instructions carefully. Respond using markdown."}, {"role": "user", "content": "你好，给我讲一个故事，大概100字"}], "stream": false, "max_tokens": 100, "temperature": 0.8, "top_p": 0.8}"
4、启动前端访问
docker run -d
--network=host
--name nginx2 --restart=always
-v $PWD/nginx/conf/nginx.conf:/etc/nginx/nginx.conf
-v $PWD/nginx/html:/usr/share/nginx/html
-v $PWD/nginx/logs:/var/log/nginx
--privileged=true
--restart=always
nginx