说明

今天使用mindie1.0部署了qwen2_7b_vl模型，测试过程出现一些问题，这里总结下。

问题1：transformers版本太低

报错信息：

[ERROR] [model_deploy_config.cpp:159] Failed to get vocab size from tokenizer wrapper with exception: ModuleNotFoundError: No module named 'transformers.models.qwen2_vl'

错误分析：qwen2_7b_vl需要transformers的版本至少为4.46.0，而我的版本是4.44。模型目录下config.json中声明的transformers版本好像是4.41, 这明显是错的。

解决办法：

pip config set global.index-url https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
pip install transformers==4.46.0

问题2：mindie的vl接口格式和openai接口不兼容

报错信息：

Invalid base64 url

标准的openai接口传输图片格式如下：

              "content": [{"type": "text", "text": text},{"type": "image_url","image_url": {"url": f"data:image/jpeg;base64,{base64_str}"}}]

而mindie支持的格式如下：

              "content": [{"type": "text", "text": text},{"type": "image_url","image_url": f"{base64_str}"}]

问题3：

报错信息：

This model's maximum input ids length cannot be greater than 2048,the input ids length is 2831

问题分析：图片加文字的token超长了，需要增加mindie的配置文件的config.json中的maxSeqLen和maxInputTokenLen、maxIterTimes。

完整代码

import os
import requests
import base64
import json
import timedef test_multimodal_model(image_path, text, model_url, model_name):# 将图片转换为base64编码with open(image_path, "rb") as image_file:encoded_image = base64.b64encode(image_file.read()).decode('utf-8')# 构建请求数据payload = {"model": model_name,"messages": [{"role": "user","content": [{"type": "text", "text": text},{"type": "image_url","image_url":  f"{encoded_image}"  #f"/path/to/pic.jpg"}]}],"max_tokens": 300}# 发送请求headers = {"Content-Type": "application/json"}start_time = time.time()  # 记录开始时间response = requests.post(model_url,headers=headers,json=payload)end_time = time.time()  # 记录结束时间print(f"请求耗时: {end_time - start_time:.2f}秒")  # 打印耗时# 返回响应if response.status_code == 200:return response.textelse:raise Exception(f"API请求失败: {response.status_code}, {response.text}<<<<")# 测试函数
if __name__ == "__main__":name = "llm_model"model_url = "http://xx.xx.xx.xx:xx/v1/chat/completions"pic_path = "./huochepiao.jpg"text = "请描述图片内容"try:result = test_multimodal_model(pic_path, text, model_url, name)print(f"result:>>>{result}")except Exception as e:print(f"发生错误: {e}")