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

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.