title: InternLM-XComposer-2.5: A Versatile Large Vision Language Model
    Supporting Long-Contextual Input and Output
summary: 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.
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paper_title: InternLM-XComposer-2.5: A Versatile Large Vision Language Model
    Supporting Long-Contextual Input and Output
paper_summary: 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.
paper_ai_keywords: large-vision language model long-contextual input and output text-image comprehension composition applications GPT-4V 7B LLM backend 24K interleaved image-text contexts RoPE extrapolation Ultra-High Resolution Understanding Fine-Grained Video Understanding Multi-Turn Multi-Image Dialogue LoRA parameters Crafting Webpages Composing High-Quality Text-Image Articles