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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 pdMAX_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 jsonif __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.jsontrain_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 jsondef process_func(example):"""将数据集进行预处理"""MAX_LENGTH = 8192input_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 = inputsresponse = 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-Instructmodel = 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LoRAconfig = 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Trainertrainer = 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 jsondef 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-Instructmodel = 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])

推理结果：

对比微调前的结果：

可以看到，微调后，整体的输出格式和coco_2014_caption保持一致，结果也和label基本相同。

训练过程中的显存占用情况：