|

Tencent Hunyuan Open-Sources Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B: A State-of-the-Art Multilingual Translation Models

Introduction

Tencent’s Hunyuan staff has launched Hunyuan-MT-7B (a translation mannequin) and Hunyuan-MT-Chimera-7B (an ensemble mannequin). Each fashions are designed particularly for multilingual machine translation and had been launched along side Tencent’s participation within the WMT2025 Normal Machine Translation shared activity, the place Hunyuan-MT-7B ranked first in 30 out of 31 language pairs.

https://github.com/Tencent-Hunyuan/Hunyuan-MT/blob/foremost/Hunyuan_MT_Technical_Report.pdf

Mannequin Overview

Hunyuan-MT-7B

  • A 7B parameter translation mannequin.
  • Helps mutual translation throughout 33 languages, together with Chinese language ethnic minority languages akin to Tibetan, Mongolian, Uyghur, and Kazakh.
  • Optimized for each high-resource and low-resource translation duties, reaching state-of-the-art outcomes amongst fashions of comparable dimension.

Hunyuan-MT-Chimera-7B

  • An built-in weak-to-strong fusion mannequin.
  • Combines a number of translation outputs at inference time and produces a refined translation utilizing reinforcement studying and aggregation methods.
  • Represents the first open-source translation mannequin of this sort, bettering translation high quality past single-system outputs.
https://github.com/Tencent-Hunyuan/Hunyuan-MT/blob/foremost/Hunyuan_MT_Technical_Report.pdf

Coaching Framework

The fashions had been skilled utilizing a five-stage framework designed for translation duties:

  1. Normal Pre-training
    • 1.3 trillion tokens masking 112 languages and dialects.
    • Multilingual corpora assessed for information worth, authenticity, and writing type.
    • Variety maintained via disciplinary, business, and thematic tagging techniques.
  2. MT-Oriented Pre-training
    • Monolingual corpora from mC4 and OSCAR, filtered utilizing fastText (language ID), minLSH (deduplication), and KenLM (perplexity filtering).
    • Parallel corpora from OPUS and ParaCrawl, filtered with CometKiwi.
    • Replay of common pre-training information (20%) to keep away from catastrophic forgetting.
  3. Supervised High quality-Tuning (SFT)
    • Stage I: ~3M parallel pairs (Flores-200, WMT take a look at units, curated Mandarin–minority information, artificial pairs, instruction-tuning information).
    • Stage II: ~268k high-quality pairs chosen via automated scoring (CometKiwi, GEMBA) and guide verification.
  4. Reinforcement Studying (RL)
    • Algorithm: GRPO.
    • Reward features:
      • XCOMET-XXL and DeepSeek-V3-0324 scoring for high quality.
      • Terminology-aware rewards (TAT-R1).
      • Repetition penalties to keep away from degenerate outputs.
  5. Weak-to-Sturdy RL
    • A number of candidate outputs generated and aggregated via reward-based output
    • Utilized in Hunyuan-MT-Chimera-7B, bettering translation robustness and decreasing repetitive errors.

Benchmark Outcomes

Computerized Analysis

  • WMT24pp (English⇔XX): Hunyuan-MT-7B achieved 0.8585 (XCOMET-XXL), surpassing bigger fashions like Gemini-2.5-Professional (0.8250) and Claude-Sonnet-4 (0.8120).
  • FLORES-200 (33 languages, 1056 pairs): Hunyuan-MT-7B scored 0.8758 (XCOMET-XXL), outperforming open-source baselines together with Qwen3-32B (0.7933).
  • Mandarin⇔Minority Languages: Scored 0.6082 (XCOMET-XXL), larger than Gemini-2.5-Professional (0.5811), displaying vital enhancements in low-resource settings.

Comparative Outcomes

  • Outperforms Google Translator by 15–65% throughout analysis classes.
  • Outperforms specialised translation fashions akin to Tower-Plus-9B and Seed-X-PPO-7B regardless of having fewer parameters.
  • Chimera-7B provides ~2.3% enchancment on FLORES-200, notably in Chinese language⇔Different and non-English⇔non-Chinese language translations.

Human Analysis

A customized analysis set (masking social, medical, authorized, and web domains) in contrast Hunyuan-MT-7B with state-of-the-art fashions:

  • Hunyuan-MT-7B: Avg. 3.189
  • Gemini-2.5-Professional: Avg. 3.223
  • DeepSeek-V3: Avg. 3.219
  • Google Translate: Avg. 2.344

This reveals that Hunyuan-MT-7B, regardless of being smaller at 7B parameters, approaches the standard of a lot bigger proprietary fashions.

Case Research

The report highlights a number of real-world instances:

  • Cultural References: Accurately interprets “小红薯” because the platform “REDnote,” in contrast to Google Translate’s “candy potatoes.”
  • Idioms: Interprets “You’re killing me” as “你真要把我笑死了” (expressing amusement), avoiding literal misinterpretation.
  • Medical Phrases: Interprets “uric acid kidney stones” exactly, whereas baselines generate malformed outputs.
  • Minority Languages: For Kazakh and Tibetan, Hunyuan-MT-7B produces coherent translations, the place baselines fail or output nonsensical textual content.
  • Chimera Enhancements: Provides enhancements in gaming jargon, intensifiers, and sports activities terminology.

Conclusion

Tencent’s launch of Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B establishes a brand new normal for open-source translation. By combining a rigorously designed coaching framework with specialised give attention to low-resource and minority language translation, the fashions obtain high quality on par with or exceeding bigger closed-source techniques. The launch of those 2 fashions offers the AI analysis neighborhood with accessible, high-performance instruments for multilingual translation analysis and deployment.


Try the Paper, GitHub Page, and Model on Hugging Face. All credit score for this analysis goes to the researchers of this venture. Be happy to take a look at our GitHub Page for Tutorials, Codes and Notebooks. Additionally, be at liberty to observe us on Twitter and don’t overlook to hitch our 100k+ ML SubReddit and Subscribe to our Newsletter.

The put up Tencent Hunyuan Open-Sources Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B: A State-of-the-Art Multilingual Translation Models appeared first on MarkTechPost.

Similar Posts