【Python】vllm部署调用Qwen3-VL

 

 

 

1. 创建环境

conda create -n vllm python=3.10

2. 安装vllm

pip install -U vllm --pre --extra-index-url https://wheels.vllm.ai/nightly

3. 下载模型

modelscope download --model Qwen/Qwen3-VL-32B-Instruct

4. 启动模型

vllm serve Qwen/Qwen3-VL-32B-Instruct --tensor-parallel-size 2 --max-model-len 16384

5. 调用，读取本地图片转为base64传入模型

import time
from openai import OpenAI
import base64

client = OpenAI(
    api_key="EMPTY",
    base_url="http://127.0.0.1:8000/v1",
    timeout=3600
)

# 处理多模态输入（示例：文本+图像）
def image_to_base64(image_path):
    with open(image_path, "rb") as img_file:
        return base64.b64encode(img_file.read()).decode('utf-8')

messages=[{"role": "system", "content": "你是一个有用的助手，请确保提供的所有信息都是准确、真实和可靠的。"}]


messages.append(
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "描述这张图片"},
            {
                "type": "image_url",
                "image_url": {
                    "url": f"data:image/jpeg;base64,{image_to_base64('demo.jpeg')}"
                }
            }
        ]
    }
)

start = time.time()
response = client.chat.completions.create(
    model="Qwen/Qwen3-VL-32B-Instruct",
    messages=messages,
    stream=True,
    max_tokens=4096
)

full_response = ""
for chunk in response:
    if chunk.choices[0].delta.content is not None:
        content = chunk.choices[0].delta.content
        print(content, end="", flush=True)  # 实时输出，不换行
        full_response += content