Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model

Xu, Yang Liu, Langping He, Xucheng Huang, Ling Jiang

Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model: 20 upvotes on Hugging Face Daily Papers, #2 of 4 papers on 2024-05-16. Day-by-day upvote history.

We introduce Xmodel-VLM, a cutting-edge multimodal vision language model. It is designed for efficient deployment on consumer GPU servers. Our work directly confronts a pivotal industry issue by grappling with the prohibitive service costs that hinder the broad adoption of large-scale multimodal systems. Through rigorous training, we have developed a 1B-scale language model from the ground up, employing the LLaVA paradigm for modal alignment. The result, which we call Xmodel-VLM, is a lightweight yet powerful multimodal vision language model. Extensive testing across numerous classic multimodal benchmarks has revealed that despite its smaller size and faster execution, Xmodel-VLM delivers performance comparable to that of larger models. Our model checkpoints and code are publicly available on GitHub at https://github.com/XiaoduoAILab/XmodelVLM.

Paper page on Hugging Face · arXiv

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