镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
47 行
1.2 KiB
Markdown
47 行
1.2 KiB
Markdown
# 使用VLLM
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## 1. 首先启动 VLLM，自行选择模型
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```
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python -m vllm.entrypoints.openai.api_server --model /home/hmp/llm/cache/Qwen1___5-32B-Chat --tensor-parallel-size 2 --dtype=half
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```
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这里使用了存储在 `/home/hmp/llm/cache/Qwen1___5-32B-Chat` 的本地模型，可以根据自己的需求更改。
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## 2. 测试 VLLM
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```
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curl http://localhost:8000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "/home/hmp/llm/cache/Qwen1___5-32B-Chat",
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"messages": [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "怎么实现一个去中心化的控制器?"}
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]
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}'
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```
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## 3. 配置本项目
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```
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API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"
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LLM_MODEL = "vllm-/home/hmp/llm/cache/Qwen1___5-32B-Chat(max_token=4096)"
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API_URL_REDIRECT = {"https://api.openai.com/v1/chat/completions": "http://localhost:8000/v1/chat/completions"}
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```
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```
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"vllm-/home/hmp/llm/cache/Qwen1___5-32B-Chat(max_token=4096)"
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其中
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"vllm-" 是前缀（必要）
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"/home/hmp/llm/cache/Qwen1___5-32B-Chat" 是模型名（必要）
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"(max_token=6666)" 是配置（非必要）
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```
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## 4. 启动！
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```
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python main.py
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```
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