InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output
| Autor: | Zhang, Pan, Dong, Xiaoyi, Zang, Yuhang, Cao, Yuhang, Qian, Rui, Chen, Lin, Guo, Qipeng, Duan, Haodong, Wang, Bin, Ouyang, Linke, Zhang, Songyang, Zhang, Wenwei, Li, Yining, Gao, Yang, Sun, Peng, Zhang, Xinyue, Li, Wei, Li, Jingwen, Wang, Wenhai, Yan, Hang, He, Conghui, Zhang, Xingcheng, Chen, Kai, Dai, Jifeng, Qiao, Yu, Lin, Dahua, Wang, Jiaqi |
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| Rok vydání: | 2024 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Technical Report. https://github.com/InternLM/InternLM-XComposer |
| Databáze: | arXiv |
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