🚀🚀🚀We are excited to open-source Tencent-HY-MT1.5, featuring two translation models—1.8B and 7B—designed for seamless on-device and cloud deployment with industry-leading speed and accuracy.
Highlights:
🔹 1.8B On-Device Power: Optimized for consumer hardware with a 1GB memory footprint. Using on-policy distillation to align with larger models, it delivers 0.18s latency (50 tokens), outperforming mainstream commercial APIs.
🔹 7B SOTA Performance: An upgraded version of our WMT25 champion, surpassing mid-sized open-source models and rivaling the 90th percentile of closed-source giants like Gemini-3.0-Pro.
🔹 33+ Languages & Dialects: High-fidelity translation across 33 languages and 5 Chinese dialects.
🔹 Production-Ready: Native support for custom terminology, long-dialogue context, and maintaining document formatting.
Already powering multiple Tencent services, our dual-model synergy ensures consistent and stable performance across both on-device and cloud environments. 🌍
👉🏻 Try it now: https://t.co/MOGj8Uwzwu
🔗 GitHub: https://t.co/a65YZGBj7B
🤗 Hugging Face:
Now Tencent-HY-MT1.5-1.8B is the #1 trending model on Hugging Face! 🥇📈 Huge thanks to the community for the support! https://t.co/FWKn70KSIW
distillation — Training a small, cheap model to imitate a big one's outputs, keeping much of the capability at a fraction of the cost.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
The on-device and cloud models share a lineage, so you can fall back between local and hosted translation without output drift. The 1.8B claims 0.18s for 50 tokenThe chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.Full definition → inside a 1GB footprint.
