Infinity-MM: Scaling Multimodal Performance with Large-Scale and
  High-Quality Instruction Data
Authors: name: Shuhao Gu, name: Jialing Zhang, name: Siyuan Zhou, name: Kevin Yu, name: Zhaohu Xing, name: Liangdong Wang, name: Zhou Cao, name: Jintao Jia, name: Zhuoyi Zhang, name: Yixuan Wang, name: Zhenchong Hu, name: Bo-Wen Zhang, name: Jijie Li, name: Dong Liang, name: Yingli Zhao, name: Yulong Ao, name: Yaoqi Liu, name: Fangxiang Feng, name: Guang Liu

Vision-Language Models (VLMs) have recently made significant progress, but
the limited scale and quality of open-source instruction data hinder their
performance compared to closed-source models. In this work, we address this
limitation by introducing Infinity-MM, a large-scale multimodal instruction
dataset with 40 million samples, enhanced through rigorous quality filtering
and deduplication. We also propose a synthetic instruction generation method
based on open-source VLMs, using detailed image annotations and diverse
question generation. Using this data, we trained a 2-billion-parameter VLM,
Aquila-VL-2B, achieving state-of-the-art (SOTA) performance for models of
similar scale. This demonstrates that expanding instruction data and generating
synthetic data can significantly improve the performance of open-source models.