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Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild

About

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. 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. Moreover, when paired with AngelSlim's 1.25-bit extreme quantization for on-device deployment, the lightweight 1.8B model requires only 440 MB of storage and achieves a 1.5x inference speedup.

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• 2026

Related benchmarks

TaskDatasetResultRank
Instruction FollowingIFBench
IFBench Score50.67
56
Machine TranslationWMT General 2025
XCOMET-XXL Score63.86
31
Machine TranslationMandarin ⇔ Minority (General)
XCOMET-XXL Score62.44
30
Machine TranslationDomainMTBench
XCOMET (Finance)97.08
30
Machine TranslationWildMTBench
XCOMET90.28
30
Machine TranslationFLORES-200 ZH ⇔ XX
XCOMET-XXL89.83
30
Machine TranslationFLORES-200 XX ⇔ XX
XCOMET-XXL Score87.47
30
Translation instruction-followingIFMTBench
Simple Score90.2
18
Instruction FollowingIFEval
IFEval Score89.8
18
Multi-turn Instruction FollowingMulti-IF
Turn 1 Score90.1
18
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