InternLM-XComposer-2.5: A Versatile Large Vision Language Model
  Supporting Long-Contextual Input and Output
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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InternLM-XComposer-2.5: A Versatile Large Vision Language Model
  Supporting Long-Contextual Input and Output
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.
A large-vision language model with long-contextual capabilities supports various comprehension and composition tasks, achieving GPT-4V level performance using 7B parameters.
large-vision language model long-contextual input long-contextual output text-image comprehension text-image composition RoPE extrapolation Ultra-High Resolution Understanding Fine-Grained Video Understanding Multi-Turn Multi-Image Dialogue LoRA parameters