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

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arxiv 2405.09215 v3 pith:ELLKTRLV submitted 2024-05-15 cs.CV cs.AI

classification cs.CVcs.AI
keywords modelmultimodallanguagexmodel-vlmvisiongithubacrossadoption
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Finetuning Vision-Language Models as OCR Systems for Low-Resource Languages: A Case Study of Manchu

    cs.CV 2025-07 reject novelty 5.0 of 10

    Fine-tuning LLaMA-3.2-11B on synthetic Manchu images yields an OCR system that reportedly beats a CRNN baseline on real handwritten documents, but test-set-based checkpoint selection and unclear data splits inflate the claim.

  2. Vision-Language Models for Edge Networks: A Comprehensive Survey

    cs.CV 2025-02 reject novelty 2.0 of 10

    A survey of lightweight vision-language models for edge deployment, marred by citation errors, self-citation, and a lack of selection methodology.

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