Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 21 May 2026.
Builds on
9 of 15 references.
References · 9
- 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 9,379
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
- 2023Instruction-Following Evaluation for Large Language Modelscited by 1,228
- 2024Multi-IF: Benchmarking LLMs on Multi-Turn and Multilingual Instructions Followingcited by 102
- 2024BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillationcited by 84
- 2025ParetoQ: Improving Scaling Laws in Extremely Low-bit LLM Quantizationcited by 46
- 2025MaXIFE: Multilingual and Cross-lingual Instruction Following Evaluationcited by 7
- 2026AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compressioncited by 6
- 2026Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsificationcited by 5
Built on by
Citing papers per year: 2026: 10.
10 papers.
- 2026IFMTBench: A Comprehensive Benchmark for Multilingual Translation Instruction Followingcited by 1
- 2026Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustnesscited by 1
- 2026Last Translation Benchmarkcited by 1
- 2026HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domainscited by 0
- 2026Studying quantization trade-offs for efficient inference deployment in machine translationcited by 0
- 2026Scalable Frequency- and Length-Aware Subdocument Deduplication for Large Language Model Pretrainingcited by 0
- 2026Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translationcited by 0
- 2026Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Rerankingcited by 0
- 2026VISTA-Bench: Benchmarking Multilingual Image Translation with Image-Specific Rubricscited by 0
- 2026Index-Translate: A Multilingual Translation Model Family -- Text, Speech, Controlled Dubbing, and Long-Document Translationcited by 0
- 2026Index-Translate: A Multilingual Translation Model Family -- Text, Speech, Controlled Dubbing, and Long-Document Translation30 Sep 2026
- 2026VISTA-Bench: Benchmarking Multilingual Image Translation with Image-Specific Rubrics29 Sep 2026
- 2026Last Translation Benchmark3 Sep 2026
- 2026Quit While You're Ahead: Quit for Efficient Candidate Generation in Machine Translation Reranking1 Sep 2026
- 2026Reference-Free Post-Training of Open Large Language Models for Multilingual Machine Translation11 Aug 2026
- 2026Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness10 Aug 2026
- 2026Scalable Frequency- and Length-Aware Subdocument Deduplication for Large Language Model Pretraining4 Aug 2026
- 2026Studying quantization trade-offs for efficient inference deployment in machine translation31 Jul 2026
- 2026HardMTBench: Stress-Testing Chinese-English Translation on Knowledge-Intensive Domains27 May 2026
- 2026IFMTBench: A Comprehensive Benchmark for Multilingual Translation Instruction Following27 May 2026
Similar, not cited
Close in wording, no citation either way
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- 2024GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
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