Tencent's Hunyuan team officially released and open-sourced the Hy-MT2 multilingual translation model family on May 21. The model includes three size tiers — 1.8B, 7B, and 30B-A3B (MoE) — natively supporting 33 languages of mutual translation and translation instruction following in multiple languages, with weights and technical reports public on GitHub and Hugging Face.
From a technical-path perspective, Hy-MT2 continues the "teacher-student" framework that Hunyuan recently verified on Hy3-preview: using Hy3-preview as a strong teacher, first doing MT-direction medium training to transform a general large model into a "good at translation" base version, then through family-centric post-training fine-tuning the three size tiers respectively. The core methods include Reference-Guided On-Policy Distillation, Family-Specific RL, and cross-family distillation, making the 7B and 30B models surpass DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking mode.
More noteworthy is the 1.8B on-device model. Through AngelSlim 1.25-bit extreme-low-bit quantization, it compresses the model volume to 440MB and improves inference speed by 1.5×, while overall translation quality still outperforms mainstream commercial APIs like Microsoft Translator and ByteDance's Doubao. This means the 1.8B on-device model has the engineering feasibility to "locally replace commercial APIs," a dimensionality-reduction strike against translation SaaS, cross-border e-commerce, and localization toolchains. In real business scenarios such as finance, law, and medicine, as well as complex instruction following, Hy-MT2 also maintains stable leadership. This is one of the few translation models where "open-source model + commercial-grade quality + on-device deployable" stand together, also confirming Hunyuan's product judgment in 2026 to "make large models into infrastructure."