Papers
arxiv:2605.22064

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

Published on May 21
Authors:
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,
,

Abstract

Hy-MT2 is a multilingual translation model family with three sizes supporting 33 languages, featuring efficient on-device deployment through extreme quantization and superior performance across various translation tasks.

AI-generated summary

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.

Community

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2605.22064
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 12

Browse 12 models citing this paper

Datasets citing this paper 0

No dataset linking this paper

Cite arxiv.org/abs/2605.22064 in a dataset README.md to link it from this page.

Spaces citing this paper 1

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.