下载模型:

bash
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modelscope download Qwen/Qwen2.5-VL-7B-Instruct --local_dir ./Qwen/Qwen2.5-VL-7B-Instruct

模型位置：

bash
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/mnt/jfs/model/Qwen/Qwen2.5-VL-7B-Instruct

开启api：

bash
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python -m vllm.entrypoints.openai.api_server \
--model /mnt/jfs/model/Qwen/Qwen2.5-VL-7B-Instruct \
--gpu_memory_utilization 0.9 \
--tensor_parallel_size 2 \
--served-model-name gpt \
--port 8000

请求：

bash
展开代码
import base64
import requests

def analyze_custom_image():
    # 配置服务器信息
    server_ip = "10.130.18.35"
    port = 8000
    api_endpoint = f"http://{server_ip}:{port}/v1/chat/completions"

    # 认证信息（需与服务器启动参数一致）
    api_key = "token-abc123"  # 替换为实际API密钥

    # 图片URL
    image_url = "https://img1.baidu.com/it/u=3132077889,2667724444&fm=253&fmt=auto&app=138&f=JPEG?w=800&h=1200"
    
    # 提示文本
    text_a = "请分析这张图片"

    try:
        # 1. 下载网络图片并编码为base64
        response = requests.get(image_url)
        response.raise_for_status()
        base64_data = base64.b64encode(response.content).decode('utf-8')

        # 2. 构建请求头
        headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

        # 3. 组装请求体
        payload = {
            "model": "gpt",  # 确认模型名称与服务器一致
            "messages": [
                {
                    "role": "user",
                    "content": [
                        {"type": "text", "text": text_a},
                        {
                            "type": "image_url",
                            "image_url": {
                                # 修改MIME类型为image/jpeg
                                "url": f"data:image/jpeg;base64,{base64_data}"
                            }
                        }
                    ]
                }
            ],
            "max_tokens": 4096  # 增加token限额以获取更详细描述
        }

        # 4. 发送请求
        response = requests.post(api_endpoint, headers=headers, json=payload)
        response.raise_for_status()  # 自动抛出HTTP错误

        # 5. 解析结果
        result = response.json()["choices"][0]["message"]["content"]
        return f"图像分析结果：\n{result}"

    except requests.exceptions.RequestException as e:
        return f"网络请求失败：{str(e)}"
    except KeyError:
        return f"响应格式异常，原始响应：{response.text}"
    except Exception as e:
        return f"未知错误：{str(e)}"

# 执行分析
if __name__ == "__main__":
    analysis_result = analyze_custom_image()
    print(analysis_result)