Hy-MT2 多语言翻译模型发布：1.8B 量化后仅 440MB，性能超微软豆包 API

通过 AngelSlim 1.25-bit 极端量化，1.

8B 模型仅需 440MB 存储，推理速度提升 1.5 倍，适合端侧部署。

在通用、商业、领域和指令跟随翻译任务中，7B 和 30B 模型在快速思考模式下超越 DeepSeek-V4-Pro 和 Kimi K2.6 等开源模型，1.

8B 模型整体也优于微软和豆包等主流商业 API。该工作为多语言翻译提供了高效且强大的新选择。

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

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.

This report is categorized under AITOP's "Models" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include 多语言翻译, Hy-MT2, 端侧部署, 量化.

Primary source verification: published directly by arXiv: DeepSeek with first-party authority. Cross-source corroboration: 2 independent sources have covered this development (including 岚叔), confirming industry-wide signal strength.