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.

翻译：我们提出了Xmodel-VLM，一种前沿的多模态视觉语言模型。它专为在消费级GPU服务器上高效部署而设计。我们的工作直面一个关键的行业问题，即通过应对阻碍大规模多模态系统广泛采用的高昂服务成本。通过严格训练，我们从零开始开发了一个10亿参数规模的语言模型，采用LLaVA范式进行模态对齐。由此产生的模型，我们称之为Xmodel-VLM，是一个轻量级但功能强大的多模态视觉语言模型。在众多经典多模态基准测试上的广泛测试显示，尽管模型规模更小、执行速度更快，Xmodel-VLM在性能上可与大型模型相媲美。我们的模型检查点和代码已在GitHub（https://github.com/XiaoduoAILab/XmodelVLM）上公开提供。