InternLM-XComposer-2.5: A Versatile Large Vision Language Model
  Supporting Long-Contextual Input and Output
Authors: name: Pan Zhang; name: Xiaoyi Dong; name: Yuhang Zang; name: Yuhang Cao; name: Rui Qian; name: Lin Chen; name: Qipeng Guo; name: Haodong Duan; name: Bin Wang; name: Linke Ouyang; name: Songyang Zhang; name: Wenwei Zhang; name: Yining Li; name: Yang Gao; name: Peng Sun; name: Xinyue Zhang; name: Wei Li; name: Jingwen Li; name: Wenhai Wang; name: Hang Yan; name: Conghui He; name: Xingcheng Zhang; name: Kai Chen; name: Jifeng Dai; name: Yu Qiao; name: Dahua Lin; name: Jiaqi Wang
Published: 2024-07-03 17:59:21

Abstract: We present InternLM-XComposer-2.5 (IXC-2.5), a versatile large-vision
language model that supports long-contextual input and output. IXC-2.5 excels
in various text-image comprehension and composition applications, achieving
GPT-4V level capabilities with merely 7B LLM backend. Trained with 24K
interleaved image-text contexts, it can seamlessly extend to 96K long contexts
via RoPE extrapolation. This long-context capability allows IXC-2.5 to excel in
tasks requiring extensive input and output contexts. Compared to its previous
2.0 version, InternLM-XComposer-2.5 features three major upgrades in
vision-language comprehension: (1) Ultra-High Resolution Understanding, (2)
Fine-Grained Video Understanding, and (3) Multi-Turn Multi-Image Dialogue. In
addition to comprehension, IXC-2.5 extends to two compelling applications using
extra LoRA parameters for text-image composition: (1) Crafting Webpages and (2)
Composing High-Quality Text-Image Articles. IXC-2.5 has been evaluated on 28
benchmarks, outperforming existing open-source state-of-the-art models on 16
benchmarks. It also surpasses or competes closely with GPT-4V and Gemini Pro on
16 key tasks. The InternLM-XComposer-2.5 is publicly available at
https://github.com/InternLM/InternLM-XComposer.