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:
- 1.3 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.
- 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.
- 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.
- Algorithm: GRPO.
- Reward functions:
- XCOMET-XXL and DeepSeek-V3-0324 scoring for quality.
- Terminology-aware rewards (TAT-R1).
- Repetition penalties to avoid degenerate outputs.
- 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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