SimonPu/
Hunyuan-MT-Chimera-7B

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The Hunyuan Translation Model comprises a translation model, Hunyuan-MT-7B, and an ensemble model, Hunyuan-MT-Chimera. The translation model is used to translate source text into the target language, while the ensemble model integrates multiple translatio

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Hunyuan-MT-Chimera-7B:Q8

8.0GB · 256K context window · Text · 3 days ago

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Model Introduction

The Hunyuan Translation Model comprises a translation model, Hunyuan-MT-7B, and an ensemble model, Hunyuan-MT-Chimera. The translation model is used to translate source text into the target language, while the ensemble model integrates multiple translation outputs to produce a higher-quality result. It primarily supports mutual translation among 33 languages, including five ethnic minority languages in China.

Key Features and Advantages

  • In the WMT25 competition, the model achieved first place in 30 out of the 31 language categories it participated in.
  • Hunyuan-MT-7B achieves industry-leading performance among models of comparable scale
  • Hunyuan-MT-Chimera-7B is the industry’s first open-source translation ensemble model, elevating translation quality to a new level
  • A comprehensive training framework for translation models has been proposed, spanning from pretrain → cross-lingual pretraining (CPT) → supervised fine-tuning (SFT) → translation enhancement → ensemble refinement, achieving state-of-the-art (SOTA) results for models of similar size

Related News

  • 2025.9.1 We have open-sourced Hunyuan-MT-7B , Hunyuan-MT-Chimera-7B on Hugging Face.

模型链接

Model Name Description Download
Hunyuan-MT-7B Hunyuan 7B translation model 🤗 Model
Hunyuan-MT-7B-fp8 Hunyuan 7B translation model，fp8 quant 🤗 Model
Hunyuan-MT-Chimera Hunyuan 7B translation ensemble model 🤗 Model
Hunyuan-MT-Chimera-fp8 Hunyuan 7B translation ensemble model，fp8 quant 🤗 Model

Citing Hunyuan-MT:

@misc{hunyuan_mt,
      title={Hunyuan-MT Technical Report}, 
      author={Mao Zheng and Zheng Li and Bingxin Qu and Mingyang Song and Yang Du and Mingrui Sun and Di Wang},
      year={2025},
      eprint={2509.05209},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.05209}, 
}