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