Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
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Computer Science > Computation and Language
arXiv:2605.22064 (cs)
[Submitted on 21 May 2026]
Title:Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
Authors:Mao Zheng, Zheng Li, Tao Chen, Bo Lv, Mingrui Sun, Mingyang Song, Jinlong Song, Hong Huang, Decheng Wu, Hai Wang, Yifan Song, Yanfeng Chen, Guanwei Zhang, Guanghua Yu, Yi Su, Hong Liu, Jinxiang Ou, Keyao Wang, Weile Chen, Haozhao Kuang, Kai Wang, Nuo Chen, Zihao Zheng, Chenhao Wang, Bin Xing, Chengcheng Xu, Tinghao Yu, Binghong Wu, Long Xu, Jiacheng Shi, Yunhao Wang, Baifang Chen, Lei Zhang, Qi Yang, Zhao Wu, Jiacheng Li, Lan Jiang, Lanrui Wang, Kai Zhang, Shuaipeng Li, Zhongzhi Chen, Weixuan Sun, Jiaqi Zhu, An Wang, Wei Li, Jun Xia, Weidong Han, Wutian Yang, Litong Hui, Luoguo Jia, Jiajia Wu, Xinpeng Zhou, Tianxiang Fei
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Abstract:Hy-MT2 is a family of fast-thinking multilingual translation models designed for complex real-world scenarios. It includes three model sizes: 1.8B, 7B, and 30B-A3B (MoE), all of which support translation among 33 languages and effectively follow translation instructions in multiple languages. For on-device deployment, with AngelSlim 1.25-bit extreme quantization, the 1.8B model requires only 440 MB of storage and improves inference speed by 1.5x. Multi-dimensional evaluations show that Hy-MT2 delivers outstanding performance across general, real-world business, domain-specific, and instruction-following translation tasks. The 7B and 30B models outperform open-source models such as DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking mode, while the lightweight 1.8B model also surpasses mainstream commercial APIs from providers such as Microsoft and Doubao overall.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.22064 [cs.CL] |
| (or arXiv:2605.22064v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22064
arXiv-issued DOI via DataCite (pending registration)
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