QwenLM/Qwen2.5-VL: Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.

安装Qwen2.5-VL依赖

使用Conda安装

创建conda环境

conda create -n qwen python=3.10
# 进入环境
conda activate qwen

# 从transformers库安装依赖
pip install git+https://github.com/huggingface/transformers accelerate
# 安装视频读取加速工具
pip install qwen-vl-utils[decord] -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

注意如果是Linux系统，无法从PyPI安装decord，因此使用pip install qwen-vl-utils ，这将使用torchvision来进行视频处理

github下载库安装web_demo

如果需要使用web_demo可以进行如下步骤，否则可以跳过。

git clone git@github.com:QwenLM/Qwen2.5-VL.git

如果出现timeout可以在站长之家使用ping检测github.com，修改自己host。

安装web_demo依赖

注意在这里我们如果已经安装了torch需要把txt中torch相关的删掉，因为清华源的torch不支持gpu！如果没有安装torch我们就不用清华源即可。

cd Qwen2.5-VL
pip install -r ./requirements_web_demo.txt -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple

运行demo

使用huggingface api下载模型

如果使用transformers 下载失败可以使用huggingface_hub 下载到本地：

pip install --upgrade huggingface_hub

首先在命令行登录huggingface

huggingface-cli login

具体的token创建请自行在huggingface上完成

创建download.py

import os
from huggingface_hub import snapshot_download

os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'

# 设置模型仓库 ID 和本地保存路径
repo_id = "Qwen/Qwen2.5-VL-7B-Instruct"  # 模型仓库 ID
local_dir = "./models/qwen2.5-vl"  # 本地保存路径

# 下载完整模型目录
snapshot_download(
    repo_id=repo_id,
    local_dir=local_dir,
    local_dir_use_symlinks=False,  # 避免使用符号链接
    resume_download=True,  # 支持断点续传
    # allow_patterns=["*"],  # 下载所有文件
    # ignore_patterns=["*.safetensors", "*.msgpack", "*.h5", "*.ot"],  # 可选：排除某些文件类型
    max_workers=8,  # 并发下载线程数
)

等待下载完成即可

使用ModelScope下载

通义千问2.5-VL-7B-Instruct · 模型库

pip install modelscope -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
modelscope download --model Qwen/Qwen2.5-VL-7B-Instruct --local_dir ./models

测试demo

创建quickstart.py:

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
from qwen_vl_utils import process_vision_info

# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-7B-Instruct", torch_dtype="auto", device_map="auto"
)

# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
#     "Qwen/Qwen2.5-VL-7B-Instruct",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processor
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to(model.device)

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)