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

TL;DR

Xmodel-VLM is a lightweight, efficient multimodal vision language model that achieves comparable performance to larger models, addressing industry cost issues and enabling broader adoption.

Contribution

We present Xmodel-VLM, a novel small-scale multimodal model trained with the LLaVA paradigm, offering high performance with reduced computational requirements.

Findings

Achieves performance comparable to larger models on multiple benchmarks.

Designed for efficient deployment on consumer GPU servers.

Reduces service costs for large-scale multimodal systems.

Abstract

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.