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【多模态大模型实战】使用LoRA微调Qwen2.5-VL

news 来源：原创 2025/6/23 15:12:08

1 前言

今年一月份，阿里发布了Qwen2.5-VL，Qwen 模型家族的旗舰视觉语言模型，对比此前发布的 Qwen2-VL 实现了巨大的飞跃。

主要特点：

感知更丰富的世界：Qwen2.5-VL 不仅擅长识别常见物体，如花、鸟、鱼和昆虫，还能够分析图像中的文本、图表、图标、图形和布局。
Agent：Qwen2.5-VL 直接作为一个视觉 Agent，可以推理并动态地使用工具，初步具备了使用电脑和使用手机的能力。
理解长视频和捕捉事件：Qwen2.5-VL 能够理解超过 1 小时的视频，并且这次它具备了通过精准定位相关视频片段来捕捉事件的新能力。
视觉定位：Qwen2.5-VL 可以通过生成 bounding boxes 或者 points 来准确定位图像中的物体，并能够为坐标和属性提供稳定的 JSON 输出。
结构化输出：对于发票、表单、表格等数据，Qwen2.5-VL 支持其内容的结构化输出，惠及金融、商业等领域的应用。

研究者对视觉语言模型进行了全面的评估，比较了 SOTA 模型以及同尺寸规模模型中表现最好的模型。在旗舰模型 Qwen2.5-VL-72B-Instruct 的测试中，它在一系列涵盖多个领域和任务的基准测试中表现出色，包括大学水平的问题、数学、文档理解、视觉问答、视频理解和视觉 Agent。值得注意的是，Qwen2.5-VL 在理解文档和图表方面具有显著优势，并且能够作为视觉 Agent 进行操作，而无需特定任务的微调。

GitHub：https://github.com/QwenLM/Qwen2.5-VL
技术报告：https://arxiv.org/pdf/2502.13923
HuggingFace：https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5
ModelScope：https://modelscope.cn/collections/Qwen25-VL-58fbb5d31f1d47

本文将直接使用 Qwen2.5-VL-7B-Instruct 模型在 coco_2014_caption 数据集上进行LoRA微调训练，旨在熟悉Qwen2.5-VL数据处理和训练流程。
GitHub地址：https://github.com/Donvink/Qwen2.5-VL-Finetune

2 环境配置

我们可以直接使用Open-R1的环境，或者新建一个虚拟环境，安装需要的依赖库（python >= 3.9）:

pip install torch transformers datasets peft accelerate qwen-vl-utils swanlab modelscope pandas

在运行代码的时候，如果导入modelscope库报错，可以重新执行以下命令安装modelscope：

pip install modelscope[framework]

或者

pip install modelscope[dataset]

3 数据处理

首先，我们将coco_2014_caption数据集中的图片和label下载到本地：

# 导入所需的库
from modelscope.msdatasets import MsDataset
import os
import pandas as pd

MAX_DATA_NUMBER = 500

# 检查目录是否已存在
if not os.path.exists('coco_2014/coco_2014_caption'):
    # 从modelscope下载COCO 2014图像描述数据集
    ds =  MsDataset.load('modelscope/coco_2014_caption', subset_name='coco_2014_caption', split='train')
    print(len(ds))
    # 设置处理的图片数量上限
    total = min(MAX_DATA_NUMBER, len(ds))

    # 创建保存图片的目录
    os.makedirs('coco_2014/coco_2014_caption', exist_ok=True)

    # 初始化存储图片路径和描述的列表
    image_paths = []
    captions = []

    for i in range(total):
        # 获取每个样本的信息
        item = ds[i]
        image_id = item['image_id']
        caption = item['caption']
        image = item['image']

        # 保存图片并记录路径
        image_path = os.path.abspath(f'coco_2014/coco_2014_caption/{image_id}.jpg')
        image.save(image_path)

        # 将路径和描述添加到列表中
        image_paths.append(image_path)
        captions.append(caption)

        # 每处理50张图片打印一次进度
        if (i + 1) % 50 == 0:
            print(f'Processing {i+1}/{total} images ({(i+1)/total*100:.1f}%)')

    # 将图片路径和描述保存为CSV文件
    df = pd.DataFrame({
        'image_path': image_paths,
        'caption': captions
    })

    # 将数据保存为CSV文件
    df.to_csv('./coco_2014/coco-2024-dataset.csv', index=False)

    print(f'数据处理完成，共处理了{total}张图片')

else:
    print('coco_2014/coco_2014_caption目录已存在,跳过数据处理步骤')

然后，我们将原始的CSV格式转换成json格式：

import pandas as pd
import json

# 载入CSV文件
df = pd.read_csv('./coco_2014/coco-2024-dataset.csv')
conversations = []

# 添加对话数据
for i in range(len(df)):
    conversations.append({
        "id": f"identity_{i+1}",
        "conversations": [
            {
                "from": "user",
                "value": f"COCO Yes: <|vision_start|>{df.iloc[i]['image_path']}<|vision_end|>"
            },
            {
                "from": "assistant", 
                "value": df.iloc[i]['caption']
            }
        ]
    })

# 保存为Json
with open('./coco_2014/data_vl.json', 'w', encoding='utf-8') as f:
    json.dump(conversations, f, ensure_ascii=False, indent=2)

最后，我们将数据集划分成训练集和测试集：

import json

if __name__ == "__main__":
    # 处理数据集：读取json文件
    # 拆分成训练集和测试集，保存为data_vl_train.json和data_vl_test.json
    train_json_path = "coco_2014/data_vl.json"
    with open(train_json_path, 'r') as f:
        data = json.load(f)
        train_data = data[:-50]
        test_data = data[-50:]

    with open("coco_2014/data_vl_train.json", "w") as f:
        json.dump(train_data, f)

    with open("coco_2014/data_vl_test.json", "w") as f:
        json.dump(test_data, f)

4 LoRA微调

我们可以提前从ModelScope上将模型下载到本地文件夹Qwen2.5-VL-7B-Instruct中，或者也可以在训练前通过代码下载。完整的训练代码如下：

import torch
from datasets import Dataset
from modelscope import snapshot_download, AutoTokenizer
from swanlab.integration.transformers import SwanLabCallback
from qwen_vl_utils import process_vision_info
from peft import LoraConfig, TaskType, get_peft_model, PeftModel
from transformers import (
    TrainingArguments,
    Trainer,
    DataCollatorForSeq2Seq,
    Qwen2_5_VLForConditionalGeneration,
    AutoProcessor,
)
import swanlab
import json


def process_func(example):
    """
    将数据集进行预处理
    """
    MAX_LENGTH = 8192
    input_ids, attention_mask, labels = [], [], []
    conversation = example["conversations"]
    input_content = conversation[0]["value"]
    output_content = conversation[1]["value"]
    file_path = input_content.split("<|vision_start|>")[1].split("<|vision_end|>")[0]  # 获取图像路径
    messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": f"{file_path}",
                    "resized_height": 280,
                    "resized_width": 280,
                },
                {"type": "text", "text": "COCO Yes:"},
            ],
        }
    ]
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )  # 获取文本
    image_inputs, video_inputs = process_vision_info(messages)  # 获取数据（预处理过）
    inputs = processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=True,
        return_tensors="pt",
    )
    inputs = {key: value.tolist() for key, value in inputs.items()} #tensor -> list,为了方便拼接
    instruction = inputs

    response = tokenizer(f"{output_content}", add_special_tokens=False)

    input_ids = (
            instruction["input_ids"][0] + response["input_ids"] + [tokenizer.pad_token_id]
    )

    attention_mask = instruction["attention_mask"][0] + response["attention_mask"] + [1]
    labels = (
            [-100] * len(instruction["input_ids"][0])
            + response["input_ids"]
            + [tokenizer.pad_token_id]
    )
    if len(input_ids) > MAX_LENGTH:  # 做一个截断
        input_ids = input_ids[:MAX_LENGTH]
        attention_mask = attention_mask[:MAX_LENGTH]
        labels = labels[:MAX_LENGTH]

    input_ids = torch.tensor(input_ids)
    attention_mask = torch.tensor(attention_mask)
    labels = torch.tensor(labels)
    inputs['pixel_values'] = torch.tensor(inputs['pixel_values'])
    inputs['image_grid_thw'] = torch.tensor(inputs['image_grid_thw']).squeeze(0)  #由（1,h,w)变换为（h,w）
    return {"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels,
            "pixel_values": inputs['pixel_values'], "image_grid_thw": inputs['image_grid_thw']}


def predict(messages, model):
    # 准备推理
    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("cuda")

    # 生成输出
    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
    )
    
    return output_text[0]


if __name__ == "__main__":
    # # 在modelscope上下载Qwen2.5-VL模型到本地目录下
    # model_dir = snapshot_download("Qwen/Qwen2.5-VL-7B-Instruct", cache_dir="./", revision="master")
    model_path = "./Qwen2.5-VL-7B-Instruct"

    # 使用Transformers加载模型权重
    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
    processor = AutoProcessor.from_pretrained(model_path)

    # 加载 Qwen2.5-VL-7B-Instruct
    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        model_path,
        torch_dtype=torch.bfloat16,
        device_map="auto",
    )
    model.enable_input_require_grads()  # 开启梯度检查点时，要执行该方法

    train_ds = Dataset.from_json("coco_2014/data_vl_train.json")
    train_dataset = train_ds.map(process_func)

    # 配置LoRA
    config = LoraConfig(
        task_type=TaskType.CAUSAL_LM,
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
        inference_mode=False,  # 训练模式
        r=64,  # Lora 秩
        lora_alpha=16,  # Lora alaph，具体作用参见 Lora 原理
        lora_dropout=0.05,  # Dropout 比例
        bias="none",
    )

    # 获取LoRA模型
    peft_model = get_peft_model(model, config)

    # 配置训练参数
    args = TrainingArguments(
        output_dir="./output/Qwen2.5-VL-7B",
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        logging_steps=10,
        logging_first_step=5,
        num_train_epochs=2,
        save_steps=100,
        learning_rate=1e-4,
        save_on_each_node=True,
        gradient_checkpointing=True,
        report_to="none",
    )
            
    # # 设置SwanLab回调
    # swanlab_callback = SwanLabCallback(
    #     project="Qwen2.5-VL-finetune",
    #     experiment_name="qwen2-vl-coco2014",
    #     config={
    #         "model": "https://modelscope.cn/models/Qwen/Qwen2.5-VL-7B-Instruct",
    #         "dataset": "https://modelscope.cn/datasets/modelscope/coco_2014_caption/quickstart",
    #         "github": "https://github.com/datawhalechina/self-llm",
    #         "prompt": "COCO Yes: ",
    #         "train_data_number": len(train_data),
    #         "lora_rank": 64,
    #         "lora_alpha": 16,
    #         "lora_dropout": 0.1,
    #     },
    # )

    # 配置Trainer
    trainer = Trainer(
        model=peft_model,
        args=args,
        train_dataset=train_dataset,
        data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
        # callbacks=[swanlab_callback],
    )

    # 开启模型训练
    trainer.train()

    # exit()

    # ====================测试模式===================
    # 配置测试参数
    val_config = LoraConfig(
        task_type=TaskType.CAUSAL_LM,
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
        inference_mode=True,  # 测试模式
        r=64,  # Lora 秩
        lora_alpha=16,  # Lora alaph，具体作用参见 Lora 原理
        lora_dropout=0.05,  # Dropout 比例
        bias="none",
    )

    # 获取测试模型
    val_peft_model = PeftModel.from_pretrained(model, model_id="./output/Qwen2.5-VL-7B/checkpoint-56", config=val_config)

    # 读取测试数据
    with open("coco_2014/data_vl_test.json", "r") as f:
        test_dataset = json.load(f)

    test_image_list = []
    for item in test_dataset:
        input_image_prompt = item["conversations"][0]["value"]
        # 去掉前后的<|vision_start|>和<|vision_end|>
        origin_image_path = input_image_prompt.split("<|vision_start|>")[1].split("<|vision_end|>")[0]
        
        messages = [{
            "role": "user", 
            "content": [
                {
                "type": "image", 
                "image": origin_image_path
                },
                {
                "type": "text",
                "text": "COCO Yes:"
                }
            ]}]
        
        response = predict(messages, val_peft_model)
        messages.append({"role": "assistant", "content": f"{response}"})
        print(messages[-1])

        # test_image_list.append(swanlab.Image(origin_image_path, caption=response))

    # swanlab.log({"Prediction": test_image_list})

    # # 在Jupyter Notebook中运行时要停止SwanLab记录，需要调用swanlab.finish()
    # swanlab.finish()

5 结果验证

推理时，我们需要加载训练好的LoRA模型，完整的推理代码如下：

import torch
from datasets import Dataset
from modelscope import snapshot_download, AutoTokenizer
from swanlab.integration.transformers import SwanLabCallback
from qwen_vl_utils import process_vision_info
from peft import LoraConfig, TaskType, get_peft_model, PeftModel
from transformers import (
    TrainingArguments,
    Trainer,
    DataCollatorForSeq2Seq,
    Qwen2_5_VLForConditionalGeneration,
    AutoProcessor,
)
import swanlab
import json


def predict(messages, model):
    # 准备推理
    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("cuda")

    # 生成输出
    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
    )
    
    return output_text[0]


if __name__ == "__main__":
    # # 在modelscope上下载Qwen2.5-VL模型到本地目录下
    # model_dir = snapshot_download("Qwen/Qwen2.5-VL-7B-Instruct", cache_dir="./", revision="master")
    model_path = "./Qwen2.5-VL-7B-Instruct"

    # 使用Transformers加载模型权重
    tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True)
    processor = AutoProcessor.from_pretrained(model_path)

    # 加载 Qwen2.5-VL-7B-Instruct
    model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
        model_path,
        torch_dtype="auto",
        device_map="auto",
    )

    # ====================测试模式===================
    # 配置测试参数
    val_config = LoraConfig(
        task_type=TaskType.CAUSAL_LM,
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
        inference_mode=True,  # 测试模式
        r=64,  # Lora 秩
        lora_alpha=16,  # Lora alaph，具体作用参见 Lora 原理
        lora_dropout=0.05,  # Dropout 比例
        bias="none",
    )

    # 获取测试模型
    val_peft_model = PeftModel.from_pretrained(model, model_id="./output/Qwen2.5-VL-7B/checkpoint-56", config=val_config)

    # 读取测试数据
    with open("coco_2014/data_vl_test.json", "r") as f:
        test_dataset = json.load(f)

    test_image_list = []
    for item in test_dataset:
        input_image_prompt = item["conversations"][0]["value"]
        # 去掉前后的<|vision_start|>和<|vision_end|>
        origin_image_path = input_image_prompt.split("<|vision_start|>")[1].split("<|vision_end|>")[0]
        
        messages = [{
            "role": "user", 
            "content": [
                {
                "type": "image", 
                "image": origin_image_path
                },
                {
                "type": "text",
                "text": "COCO Yes:"
                }
            ]}]
        
        response = predict(messages, val_peft_model)
        messages.append({"role": "assistant", "content": f"{response}"})
        print(messages[-1])