Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
Authors: name: Wanting Xu, name: Yang Liu, name: Langping He, name: Xucheng Huang, name: Ling Jiang

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.