Xmodel-VLM：多模态视觉语言模型的简单基线
Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
AI总结：
针对大规模多模态系统服务成本高、难普及的问题，研究团队基于LLaVA范式从零训练1B规模语言模型，推出轻量高效的Xmodel-VLM多模态视觉语言模型，在消费级GPU可部署，性能媲美大模型，相关资源已开源。
摘要：
我们推出Xmodel-VLM，一款前沿的多模态视觉语言模型，专为消费级GPU服务器上的高效部署而设计。本研究直面一项关键的行业难题：高昂的服务成本阻碍了大规模多模态系统的广泛应用。经过严格训练，我们从零开发了一个10亿参数规模的语言模型，采用LLaVA（大语言与视觉助手）范式进行模态对齐，最终得到的轻量却强大的多模态视觉语言模型即为Xmodel-VLM。在多个经典多模态基准上的广泛测试表明，尽管Xmodel-VLM规模更小、运行速度更快，其性能却可与更大的模型相媲美。我们的模型检查点和代码已在GitHub公开，地址为https://github.com/XiaoduoAILab/XmodelVLM。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
