  Image-text interleaved data, consisting of multiple images and texts arranged
in a natural document format, aligns with the presentation paradigm of internet
data and closely resembles human reading habits. Recent studies have shown that
such data aids multimodal in-context learning and maintains the capabilities of
large language models during multimodal fine-tuning. However, the limited scale
and diversity of current image-text interleaved data restrict the development
of multimodal large language models. In this paper, we introduce OmniCorpus, a
10 billion-scale image-text interleaved dataset. Using an efficient data
engine, we filter and extract large-scale high-quality documents, which contain
8.6 billion images and 1,696 billion text tokens. Compared to counterparts
(e.g., MMC4, OBELICS), our dataset 1) has 15 times larger scales while
maintaining good data quality; 2) features more diverse sources, including both
English and non-English websites as well as video-centric websites; 3) is more
flexible, easily degradable from an image-text interleaved format to pure text
corpus and image-text pairs. Through comprehensive analysis and experiments, we
validate the quality, usability, and effectiveness of the proposed dataset. We
hope this could provide a solid data foundation for future multimodal model
research. Code and data are released at
https://github.com/OpenGVLab/OmniCorpus.
