TIGER-Lab/VLM2Vec-LLaVa-Next

A new checkpoint trained using 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. 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.