---
license: apache-2.0
datasets:
- TIGER-Lab/MMEB-train
language:
- en
base_model:
- llava-hf/llava-v1.6-mistral-7b-hf
library_name: transformers
---

A new checkpoint trained using [llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) with an enhanced training setup (LoRA tuning, batch size of 2048, maximum sub-dataset size of 100k). This model has shown significantly improved performance on MMEB & Flickr30K compared to the previous Phi-3.5-based model.

This repo contains the code and data for [VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks](https://arxiv.org/abs/2410.05160). In this paper, we focus on building a unified multimodal embedding model suitable for a wide range of tasks. Our approach is based on transforming an existing, well-trained Vision-Language Model (VLM) into an embedding model. The core idea is to append an [EOS] token at the end of the input sequence, which serves as the representation for the combined multimodal inputs.

## Github
 - [Github](https://github.com/TIGER-AI-Lab/VLM2Vec)


## Data

Our model is being trained on MMEB-train and evaluated on MMEB-eval with contrastive learning. We only use in-batch negatives for training. 
Our results on 36 evaluation datasets are:
### Train/Eval Data
 - Train data: https://huggingface.co/datasets/TIGER-Lab/MMEB-train
 - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-eval


## Experimental Results
VLM2Vec-LlaVa-Next could outperform the baselines and other version of VLM2Vec by a large margin.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64778fb8168cb428e00f69b0/IaKuKe5ps_bvDTf98C0rt.png)


## How to use VLM2Vec-LlaVa-Next

First you can clone our github
```bash
git clone https://github.com/TIGER-AI-Lab/VLM2Vec.git
```

Then you can enter the directory to run the following command.

from src.model import MMEBModel
from src.arguments import ModelArguments
from src.utils import load_processor

import torch
from transformers import HfArgumentParser, AutoProcessor
from PIL import Image
import numpy as np

model_args = ModelArguments(
    model_name='TIGER-Lab/VLM2Vec-Full',
    pooling='last',
    normalize=True,
    model_backbone='llava')

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

processor = load_processor(model_args)

# Image + Text -> Text
inputs = processor('<image_1|> Represent the given image with the following question: What is in the image', [Image.open('figures/example.jpg')])
inputs = {key: value.to('cuda') for key, value in inputs.items()}
qry_output = model(qry=inputs)["qry_reps"]

string = 'A cat and a dog'
inputs = processor(string)
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))
## A cat and a dog = tensor([[0.2969]], device='cuda:0', dtype=torch.bfloat16)

string = 'A cat and a tiger'
inputs = processor(string)
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))
## A cat and a tiger = tensor([[0.2080]], device='cuda:0', dtype=torch.bfloat16)

# Text -> Image
inputs = processor('Find me an everyday image that matches the given caption: A cat and a dog.',)
inputs = {key: value.to('cuda') for key, value in inputs.items()}
qry_output = model(qry=inputs)["qry_reps"]

string = '<|image_1|> Represent the given image.'
inputs = processor(string, [Image.open('figures/example.jpg')])
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))
## <|image_1|> Represent the given image. = tensor([[0.3105]], device='cuda:0', dtype=torch.bfloat16)

inputs = processor('Find me an everyday image that matches the given caption: A cat and a tiger.',)
inputs = {key: value.to('cuda') for key, value in inputs.items()}
qry_output = model(qry=inputs)["qry_reps"]

string = '<|image_1|> Represent the given image.'
inputs = processor(string, [Image.open('figures/example.jpg')])
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))
## <|image_1|> Represent the given image. = tensor([[0.2158]], device='cuda:0', dtype=torch.bfloat16)
```

## Citation
```
@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}
}