Multimodal Large Language Models (MLLMs) have demonstrated notable
capabilities in general visual understanding and reasoning tasks. However,
their deployment is hindered by substantial computational costs in both
training and inference, limiting accessibility to the broader research and user
communities. A straightforward solution is to leverage smaller pre-trained
vision and language models, which inevitably causes significant performance
drop. In this paper, we demonstrate the possibility to beat the scaling law and
train a smaller but better MLLM by exploring more informative training data.
Specifically, we introduce Bunny, a family of lightweight MLLMs with flexible
vision and language backbones for efficient multimodal learning from condensed
training data. Remarkably, our Bunny-3B outperforms the state-of-the-art large
MLLMs, especially LLaVA-v1.5-13B, on multiple benchmarks. The code, models and
data can be found in this https URL

通过探索更加信息丰富的训练数据，本文演示了击败规模定律并训练出更小但更强大的轻量级多模态大语言模型 Bunny，其背后利用了灵活的视觉和语言基础模块进行高效的多模态学习。