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://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://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://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