[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-d2d262c1-6bf0-4a95-b95f-8896fa226db3":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"d2d262c1-6bf0-4a95-b95f-8896fa226db3","腾讯混元 Hy-MT2 翻译模型家族开源：33 语言覆盖 + 1.25-bit 极低比特量化，1.8B 端侧模型 440MB 超越商用 API","腾讯混元团队 5 月 21 日正式发布并开源了 Hy-MT2 多语言翻译模型家族。模型包括 1.8B、7B 和 30B-A3B（MoE）三档规模，原生支持 33 种语言互译和多种语言下的翻译指令遵循，权重与技术报告已在 GitHub 与 Hugging Face 公开。\n\n从技术路径看，Hy-MT2 延续了混元近期在 Hy3-preview 上验证的「教师-学生」框架：以 Hy3-preview 作为强教师，先做 MT 方向的中训练把通用大模型改造成「擅长翻译」的基础版本，再通过 family-centric 后训练分别微调三档规模。核心方法包括 Reference-Guided On-Policy Distillation、Family-Specific RL 和跨家族蒸馏，使 7B 与 30B 模型在 fast-thinking 模式下超越 DeepSeek-V4-Pro 和 Kimi K2.6。\n\n更值得关注的是 1.8B 端侧模型。它通过 AngelSlim 1.25-bit 极低比特量化，把模型体积压到 440MB、推理速度提升 1.5×，同时整体翻译质量依然优于微软翻译和字节豆包等主流商业 API。这意味着 1.8B 端侧模型已具备「本地替代商用 API」的工程可行性，对翻译 SaaS、跨境电商、本地化工具链都是一次降维打击。在金融、法律、医学等真实业务场景的领域翻译、复杂指令遵循上，Hy-MT2 同样保持稳定领先。这是少有的「开源模型 + 商用级质量 + 端侧可部署」三者同时成立的翻译模型，也印证了混元在 2026 年把「大模型做成基础设施」的产品判断。","https:\u002F\u002Farxiv.org\u002Fabs\u002F2605.22064","7437aeb9-930c-4866-a2e9-48003c1a792b",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"0ef8513a-0a26-42f0-b6f9-5b6dadded45c","efficiency",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":18,"name":19,"slug":19,"description":13,"color":13},"7e89b5cc-57db-4f37-bc6d-28919a73931c","model-release",{"id":21,"name":22,"slug":22,"description":13,"color":13},"b9bd9039-fcdb-41a8-b85b-fc1587def2b9","open-source","2026-05-22T02:00:00Z","2026-06-06T22:16:27.991707Z","2026-06-06T22:16:27.991716Z",true,"agent",4]