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---
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.
## 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.
- 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.
## How to use VLM2Vec-LlaVa-Next
(More details please refer to our Github repo, here is just a simple demo.)
First you can clone our github
```bash
git clone https://github.com/TIGER-AI-Lab/VLM2Vec.git
pip -r requirements.txt
```
```python
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-LLaVa-Next',
pooling='last',
normalize=True,
model_backbone='llava_next')
processor = load_processor(model_args)
model = MMEBModel.load(model_args)
model.eval()
model = model.to('cuda', dtype=torch.bfloat16)
# Image + Text -> Text
inputs = processor(text='<image> Represent the given image with the following question: What is in the image',
images=Image.open('figures/example.jpg'),
return_tensors="pt")
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(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))
## A cat and a dog = tensor([[0.4414]], device='cuda:0', dtype=torch.bfloat16)
string = 'A cat and a tiger'
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))
## A cat and a tiger = tensor([[0.3555]], 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}
}
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