Vlm2vec V2.0

🚀 VLM2Vec-V2

VLM2Vec-V2 是一個用於大規模多模態嵌入任務的模型，通過訓練視覺語言模型，為視頻、圖像和視覺文檔等多模態數據提供更強大的嵌入能力。它在多模態評估基準（MMEB）上取得了優秀的實驗結果，具有廣泛的應用前景。

官網 |Github | 🏆排行榜 | 📖MMEB-V2/VLM2Vec-V2 論文 | | 📖MMEB-V1/VLM2Vec-V1 論文

🚀 快速開始

🌟 新特性

• [2025.07] 發佈技術報告。
• [2025.05] 首次發佈 MMEB-V2/VLM2Vec-V2。

📊 實驗結果

我們提供了在 MMEB-V2 上的實驗結果。 詳細的排行榜請見此處。

💻 使用示例

基礎用法

我們在 Github 上提供了演示示例。

from src.arguments import ModelArguments, DataArguments
from src.model.model import MMEBModel
from src.model.processor import load_processor, QWEN2_VL, VLM_VIDEO_TOKENS
import torch
from src.model.vlm_backbone.qwen2_vl.qwen_vl_utils import process_vision_info

model_args = ModelArguments(
    model_name='Qwen/Qwen2-VL-7B-Instruct',
    checkpoint_path='TIGER-Lab/VLM2Vec-Qwen2VL-7B',
    pooling='last',
    normalize=True,
    model_backbone='qwen2_vl',
    lora=True
)
data_args = DataArguments()

processor = load_processor(model_args, data_args)
model = MMEBModel.load(model_args)
model = model.to('cuda', dtype=torch.bfloat16)
model.eval()

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "assets/example_video.mp4",
                "max_pixels": 360 * 420,
                "fps": 1.0,
            },
            {"type": "text", "text": "Describe this video."},
        ],
    }
]

image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=f'{VLM_VIDEO_TOKENS[QWEN2_VL]} Represent the given video.',
    videos=video_inputs,
    return_tensors="pt"
)
inputs = {key: value.to('cuda') for key, value in inputs.items()}
inputs['pixel_values_videos'] = inputs['pixel_values_videos'].unsqueeze(0)
inputs['video_grid_thw'] = inputs['video_grid_thw'].unsqueeze(0)
qry_output = model(qry=inputs)["qry_reps"]

string = 'A man in a gray sweater plays fetch with his dog in the snowy yard, throwing a toy and watching it run.'
inputs = processor(text=string,
                   images=None,
                   return_tensors="pt")
inputs = {key: value.to('cuda') for key, value in inputs.items()}
tgt_output = model(tgt=inputs)["tgt_reps"]
print(string, '=', model.compute_similarity(qry_output, tgt_output))
## tensor([[0.4746]], device='cuda:0', dtype=torch.bfloat16)

string = 'A person dressed in a blue jacket shovels the snow-covered pavement outside their house.'
inputs = processor(text=string,
                   images=None,
                   return_tensors="pt")
inputs = {key: value.to('cuda') for key, value in inputs.items()}
tgt_output = model(tgt=inputs)["tgt_reps"]
print(string, '=', model.compute_similarity(qry_output, tgt_output))
## tensor([[0.3223]], device='cuda:0', dtype=torch.bfloat16)

📚 引用

如果您使用了該項目，請引用以下論文：

@article{jiang2024vlm2vec,
  title={VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks},
  author={Jiang, Ziyan and Meng, Rui and Yang, Xinyi and Yavuz, Semih and Zhou, Yingbo and Chen, Wenhu},
  journal={arXiv preprint arXiv:2410.05160},
  year={2024}
}

@article{meng2025vlm2vecv2,
  title={VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents},
  author={Rui Meng and Ziyan Jiang and Ye Liu and Mingyi Su and Xinyi Yang and Yuepeng Fu and Can Qin and Zeyuan Chen and Ran Xu and Caiming Xiong and Yingbo Zhou and Wenhu Chen and Semih Yavuz},
  journal={arXiv preprint arXiv:2507.04590},
  year={2025}
}

📄 許可證

本項目採用 Apache-2.0 許可證。

📦 數據集

• TIGER-Lab/MMEB-train
• TIGER-Lab/MMEB-V2
• TIGER-Lab/MMEB-eval

📋 庫名稱

transformers