Introduction

Tencent’s Hunyuan team has released Hunyuan-MT-7B (a translation model) and Hunyuan-MT-Chimera-7B (an ensemble model). Both models are designed specifically for multilingual machine translation and were introduced in conjunction with Tencent’s participation in the WMT2025 General Machine Translation shared task, where Hunyuan-MT-7B ranked first in 30 out of 31 language pairs.

Model Overview

Hunyuan-MT-7B

• A 7B parameter translation model.
• Supports mutual translation across 33 languages, including Chinese ethnic minority languages such as Tibetan, Mongolian, Uyghur, and Kazakh.
• Optimized for both high-resource and low-resource translation tasks, achieving state-of-the-art results among models of comparable size.

Hunyuan-MT-Chimera-7B

• An integrated weak-to-strong fusion model.
• Combines multiple translation outputs at inference time and produces a refined translation using reinforcement learning and aggregation techniques.
• Represents the first open-source translation model of this type, improving translation quality beyond single-system outputs.

Training Framework

The models were trained using a five-stage framework designed for translation tasks:

1. General Pre-training
   • 1.3 trillion tokens covering 112 languages and dialects.
   • Multilingual corpora assessed for knowledge value, authenticity, and writing style.
   • Diversity maintained through disciplinary, industry, and thematic tagging systems.
2. MT-Oriented Pre-training
   • Monolingual corpora from mC4 and OSCAR, filtered using fastText (language ID), minLSH (deduplication), and KenLM (perplexity filtering).
   • Parallel corpora from OPUS and ParaCrawl, filtered with CometKiwi.
   • Replay of general pre-training data (20%) to avoid catastrophic forgetting.
3. Supervised Fine-Tuning (SFT)
   • Stage I: ~3M parallel pairs (Flores-200, WMT test sets, curated Mandarin–minority data, synthetic pairs, instruction-tuning data).
   • Stage II: ~268k high-quality pairs selected through automated scoring (CometKiwi, GEMBA) and manual verification.
4. Reinforcement Learning (RL)
   • Algorithm: GRPO.
   • Reward functions:
     • XCOMET-XXL and DeepSeek-V3-0324 scoring for quality.
     • Terminology-aware rewards (TAT-R1).
     • Repetition penalties to avoid degenerate outputs.
5. Weak-to-Strong RL
   • Multiple candidate outputs generated and aggregated through reward-based output
   • Applied in Hunyuan-MT-Chimera-7B, improving translation robustness and reducing repetitive errors.

Benchmark Results

Automatic Evaluation

• WMT24pp (English⇔XX): Hunyuan-MT-7B achieved 0.8585 (XCOMET-XXL), surpassing larger models like Gemini-2.5-Pro (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 including Qwen3-32B (0.7933).
• Mandarin⇔Minority Languages: Scored 0.6082 (XCOMET-XXL), higher than Gemini-2.5-Pro (0.5811), showing significant improvements in low-resource settings.

Comparative Results

• Outperforms Google Translator by 15–65% across evaluation categories.
• Outperforms specialized translation models such as Tower-Plus-9B and Seed-X-PPO-7B despite having fewer parameters.
• Chimera-7B adds ~2.3% improvement on FLORES-200, particularly in Chinese⇔Other and non-English⇔non-Chinese translations.

Human Evaluation

A custom evaluation set (covering social, medical, legal, and internet domains) compared Hunyuan-MT-7B with state-of-the-art models:

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

This shows that Hunyuan-MT-7B, despite being smaller at 7B parameters, approaches the quality of much larger proprietary models.

Case Studies

The report highlights several real-world cases:

• Cultural References: Correctly translates “小红薯” as the platform “REDnote,” unlike Google Translate’s “sweet potatoes.”
• Idioms: Interprets “You are killing me” as “你真要把我笑死了” (expressing amusement), avoiding literal misinterpretation.
• Medical Terms: Translates “uric acid kidney stones” precisely, while baselines generate malformed outputs.
• Minority Languages: For Kazakh and Tibetan, Hunyuan-MT-7B produces coherent translations, where baselines fail or output nonsensical text.
• Chimera Enhancements: Adds improvements in gaming jargon, intensifiers, and sports terminology.

Conclusion

Tencent’s release of Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B establishes a new standard for open-source translation. By combining a carefully designed training framework with specialized focus on low-resource and minority language translation, the models achieve quality on par with or exceeding larger closed-source systems. The launch of these 2 models provides the AI research community with accessible, high-performance tools for multilingual translation research and deployment.

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