ALLaVA: Harnessing GPT4V-synthesized Data for A Lite Vision-Language
  Model
Guiming Hardy Chen Shunian Chen Ruifei Zhang Junying Chen Xiangbo Wu Zhiyi Zhang Zhihong Chen Jianquan Li Xiang Wan Benyou Wang
Recent advancements in Large Vision-Language Models (LVLMs) have enabled
processing of multimodal inputs in language models but require significant
computational resources for deployment, especially in edge devices. This study
aims to bridge the performance gap between traditional-scale LVLMs and
resource-friendly lite versions by adopting high-quality training data. To do
this, a synthetic dataset is created by leveraging GPT-4V's ability to generate
detailed captions, complex reasoning instructions and detailed answers from
images. The resulted model trained with our data, ALLaVA, achieves competitive
performance on 12 benchmarks up to 3B LVLMs. This work highlights the
feasibility of adopting high-quality data in crafting more efficient LVLMs. Our
online demo is available at \url{https://allava.freedomai.cn}.