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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
## 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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