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1、利用vllm可以显著推理加速大模型
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conda create -n vllm python=3.10
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conda activate vllm
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conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=12.1 -c pytorch -c nvidia
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2、启动推理
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python -m vllm.entrypoints.openai.api_server --tensor-parallel-size=1 --trust-remote-code --max-model-len 1024 --model THUDM/chatglm3-6b
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指定ip和端口：--host 127.0.0.1 --port 8101
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python -m vllm.entrypoints.openai.api_server --port 8101 --tensor-parallel-size=1 --trust-remote-code --max-model-len 1024 --model THUDM/chatglm3-6b
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3、测试
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curl http://127.0.0.1:8101/v1/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "THUDM/chatglm3-6b",
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"prompt": "请用20字内回复我,你今年多大了",
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"max_tokens": 20,
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"temperature": 0
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}'
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多轮对话
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curl -X POST "http://127.0.0.1:8101/v1/completions" \
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-H "Content-Type: application/json" \
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-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\"}]}"
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多轮对话
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curl -X POST "http://127.0.0.1:8101/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-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}"
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4、启动前端访问
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docker run -d \
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--network=host \
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--name nginx2 --restart=always \
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-v $PWD/nginx/conf/nginx.conf:/etc/nginx/nginx.conf \
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-v $PWD/nginx/html:/usr/share/nginx/html \
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-v $PWD/nginx/logs:/var/log/nginx \
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--privileged=true \
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--restart=always \
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nginx
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参考文档：https://docs.vllm.ai/en/latest/ |