Xmodel-VLM: A Simple Baseline for Multimodal Vision Language Model
This addresses cost barriers for deploying multimodal AI in industry, though it is incremental as it builds on the LLaVA paradigm.
The paper tackles the high service costs limiting large-scale multimodal system adoption by developing Xmodel-VLM, a 1B-scale lightweight vision language model that achieves performance comparable to larger models on classic benchmarks.
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.