{"id":35793,"date":"2025-09-03T06:21:26","date_gmt":"2025-09-03T06:21:26","guid":{"rendered":"https:\/\/youzum.net\/tencent-hunyuan-open-sources-hunyuan-mt-7b-and-hunyuan-mt-chimera-7b-a-state-of-the-art-multilingual-translation-models\/"},"modified":"2025-09-03T06:21:26","modified_gmt":"2025-09-03T06:21:26","slug":"tencent-hunyuan-open-sources-hunyuan-mt-7b-and-hunyuan-mt-chimera-7b-a-state-of-the-art-multilingual-translation-models","status":"publish","type":"post","link":"https:\/\/youzum.net\/ja\/tencent-hunyuan-open-sources-hunyuan-mt-7b-and-hunyuan-mt-chimera-7b-a-state-of-the-art-multilingual-translation-models\/","title":{"rendered":"Tencent Hunyuan Open-Sources Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B: A State-of-the-Art Multilingual Translation Models"},"content":{"rendered":"<h2 class=\"wp-block-heading\"><strong>Introduction<\/strong><\/h2>\n<p>Tencent\u2019s Hunyuan team has released <strong>Hunyuan-MT-7B<\/strong> (a translation model) and <strong>Hunyuan-MT-Chimera-7B<\/strong> (an ensemble model). Both models are designed specifically for multilingual machine translation and were introduced in conjunction with Tencent\u2019s participation in the <strong>WMT2025 General Machine Translation shared task<\/strong>, where Hunyuan-MT-7B ranked first in <strong>30 out of 31 language pairs<\/strong>.<\/p>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"603\" data-attachment-id=\"74238\" data-permalink=\"https:\/\/www.marktechpost.com\/2025\/09\/02\/tencent-hunyuan-open-sources-hunyuan-mt-7b-and-hunyuan-mt-chimera-7b-a-state-of-the-art-multilingual-translation-models\/screenshot-2025-09-02-at-9-38-09-pm-2\/\" data-orig-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.09-PM-1.png\" data-orig-size=\"1690,996\" data-comments-opened=\"1\" data-image-meta='{\"aperture\":\"0\",\"credit\":\"\",\"camera\":\"\",\"caption\":\"\",\"created_timestamp\":\"0\",\"copyright\":\"\",\"focal_length\":\"0\",\"iso\":\"0\",\"shutter_speed\":\"0\",\"title\":\"\",\"orientation\":\"0\"}' data-image-title=\"Screenshot 2025-09-02 at 9.38.09\u202fPM\" data-image-description=\"\" data-image-caption=\"\" data-medium-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.09-PM-1-300x177.png\" data-large-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.09-PM-1-1024x603.png\" src=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.09-PM-1-1024x603.png\" alt=\"\" class=\"wp-image-74238\" \/><figcaption class=\"wp-element-caption\">https:\/\/github.com\/Tencent-Hunyuan\/Hunyuan-MT\/blob\/main\/Hunyuan_MT_Technical_Report.pdf<\/figcaption><\/figure>\n<\/div>\n<h2 class=\"wp-block-heading\"><strong>Model Overview<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Hunyuan-MT-7B<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>A <strong>7B parameter translation model<\/strong>.<\/li>\n<li>Supports <strong>mutual translation across 33 languages<\/strong>, including <strong>Chinese ethnic minority languages<\/strong> such as Tibetan, Mongolian, Uyghur, and Kazakh.<\/li>\n<li>Optimized for both <strong>high-resource and low-resource translation tasks<\/strong>, achieving state-of-the-art results among models of comparable size.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Hunyuan-MT-Chimera-7B<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>An <strong>integrated weak-to-strong fusion model<\/strong>.<\/li>\n<li>Combines multiple translation outputs at inference time and produces a refined translation using reinforcement learning and aggregation techniques.<\/li>\n<li>Represents the <strong>first open-source translation model of this type<\/strong>, improving translation quality beyond single-system outputs.<\/li>\n<\/ul>\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large is-resized\"><img decoding=\"async\" width=\"1024\" height=\"427\" data-attachment-id=\"74240\" data-permalink=\"https:\/\/www.marktechpost.com\/2025\/09\/02\/tencent-hunyuan-open-sources-hunyuan-mt-7b-and-hunyuan-mt-chimera-7b-a-state-of-the-art-multilingual-translation-models\/screenshot-2025-09-02-at-9-38-59-pm-2\/\" data-orig-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.59-PM-1.png\" data-orig-size=\"1658,692\" data-comments-opened=\"1\" data-image-meta='{\"aperture\":\"0\",\"credit\":\"\",\"camera\":\"\",\"caption\":\"\",\"created_timestamp\":\"0\",\"copyright\":\"\",\"focal_length\":\"0\",\"iso\":\"0\",\"shutter_speed\":\"0\",\"title\":\"\",\"orientation\":\"0\"}' data-image-title=\"Screenshot 2025-09-02 at 9.38.59\u202fPM\" data-image-description=\"\" data-image-caption=\"\" data-medium-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.59-PM-1-300x125.png\" data-large-file=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.59-PM-1-1024x427.png\" src=\"https:\/\/www.marktechpost.com\/wp-content\/uploads\/2025\/09\/Screenshot-2025-09-02-at-9.38.59-PM-1-1024x427.png\" alt=\"\" class=\"wp-image-74240\" \/><figcaption class=\"wp-element-caption\">https:\/\/github.com\/Tencent-Hunyuan\/Hunyuan-MT\/blob\/main\/Hunyuan_MT_Technical_Report.pdf<\/figcaption><\/figure>\n<\/div>\n<h2 class=\"wp-block-heading\"><strong>Training Framework<\/strong><\/h2>\n<p>The models were trained using a <strong>five-stage framework<\/strong> designed for translation tasks:<\/p>\n<ol class=\"wp-block-list\">\n<li><strong>General Pre-training<\/strong>\n<ul class=\"wp-block-list\">\n<li>1.3 trillion tokens covering 112 languages and dialects.<\/li>\n<li>Multilingual corpora assessed for knowledge value, authenticity, and writing style.<\/li>\n<li>Diversity maintained through disciplinary, industry, and thematic tagging systems.<\/li>\n<\/ul>\n<\/li>\n<li><strong>MT-Oriented Pre-training<\/strong>\n<ul class=\"wp-block-list\">\n<li>Monolingual corpora from mC4 and OSCAR, filtered using fastText (language ID), minLSH (deduplication), and KenLM (perplexity filtering).<\/li>\n<li>Parallel corpora from OPUS and ParaCrawl, filtered with CometKiwi.<\/li>\n<li>Replay of general pre-training data (20%) to avoid catastrophic forgetting.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Supervised Fine-Tuning (SFT)<\/strong>\n<ul class=\"wp-block-list\">\n<li>Stage I: ~3M parallel pairs (Flores-200, WMT test sets, curated Mandarin\u2013minority data, synthetic pairs, instruction-tuning data).<\/li>\n<li>Stage II: ~268k high-quality pairs selected through automated scoring (CometKiwi, GEMBA) and manual verification.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Reinforcement Learning (RL)<\/strong>\n<ul class=\"wp-block-list\">\n<li>Algorithm: <strong>GRPO<\/strong>.<\/li>\n<li>Reward functions:\n<ul class=\"wp-block-list\">\n<li>XCOMET-XXL and DeepSeek-V3-0324 scoring for quality.<\/li>\n<li>Terminology-aware rewards (TAT-R1).<\/li>\n<li>Repetition penalties to avoid degenerate outputs.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n<li><strong>Weak-to-Strong RL<\/strong>\n<ul class=\"wp-block-list\">\n<li>Multiple candidate outputs generated and aggregated through reward-based output<\/li>\n<li>Applied in <strong>Hunyuan-MT-Chimera-7B<\/strong>, improving translation robustness and reducing repetitive errors.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2 class=\"wp-block-heading\"><strong>Benchmark Results<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>Automatic Evaluation<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>WMT24pp (English\u21d4XX)<\/strong>: Hunyuan-MT-7B achieved <strong>0.8585 (XCOMET-XXL)<\/strong>, surpassing larger models like Gemini-2.5-Pro (0.8250) and Claude-Sonnet-4 (0.8120).<\/li>\n<li><strong>FLORES-200 (33 languages, 1056 pairs)<\/strong>: Hunyuan-MT-7B scored <strong>0.8758 (XCOMET-XXL)<\/strong>, outperforming open-source baselines including Qwen3-32B (0.7933).<\/li>\n<li><strong>Mandarin\u21d4Minority Languages<\/strong>: Scored <strong>0.6082 (XCOMET-XXL)<\/strong>, higher than Gemini-2.5-Pro (0.5811), showing significant improvements in low-resource settings.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Comparative Results<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>Outperforms <strong>Google Translator<\/strong> by 15\u201365% across evaluation categories.<\/li>\n<li>Outperforms specialized translation models such as <strong>Tower-Plus-9B<\/strong> and <strong>Seed-X-PPO-7B<\/strong> despite having fewer parameters.<\/li>\n<li><strong>Chimera-7B<\/strong> adds ~2.3% improvement on FLORES-200, particularly in Chinese\u21d4Other and non-English\u21d4non-Chinese translations.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\"><strong>Human Evaluation<\/strong><\/h2>\n<p>A custom evaluation set (covering social, medical, legal, and internet domains) compared Hunyuan-MT-7B with state-of-the-art models:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Hunyuan-MT-7B<\/strong>: Avg. <strong>3.189<\/strong><\/li>\n<li><strong>Gemini-2.5-Pro<\/strong>: Avg. <strong>3.223<\/strong><\/li>\n<li><strong>DeepSeek-V3<\/strong>: Avg. <strong>3.219<\/strong><\/li>\n<li><strong>Google Translate<\/strong>: Avg. <strong>2.344<\/strong><\/li>\n<\/ul>\n<p>This shows that Hunyuan-MT-7B, despite being smaller at 7B parameters, approaches the quality of much larger proprietary models.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Case Studies<\/strong><\/h2>\n<p>The report highlights several real-world cases:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Cultural References<\/strong>: Correctly translates \u201c\u5c0f\u7ea2\u85af\u201d as the platform \u201cREDnote,\u201d unlike Google Translate\u2019s \u201csweet potatoes.\u201d<\/li>\n<li><strong>Idioms<\/strong>: Interprets \u201cYou are killing me\u201d as \u201c\u4f60\u771f\u8981\u628a\u6211\u7b11\u6b7b\u4e86\u201d (expressing amusement), avoiding literal misinterpretation.<\/li>\n<li><strong>Medical Terms<\/strong>: Translates \u201curic acid kidney stones\u201d precisely, while baselines generate malformed outputs.<\/li>\n<li><strong>Minority Languages<\/strong>: For Kazakh and Tibetan, Hunyuan-MT-7B produces coherent translations, where baselines fail or output nonsensical text.<\/li>\n<li><strong>Chimera Enhancements<\/strong>: Adds improvements in gaming jargon, intensifiers, and sports terminology.<\/li>\n<\/ul>\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n<p>Tencent\u2019s release of <strong>Hunyuan-MT-7B<\/strong> and <strong>Hunyuan-MT-Chimera-7B<\/strong> establishes a new standard for open-source translation. By combining a carefully designed training framework with specialized focus on <strong>low-resource and minority language translation<\/strong>, 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.<\/p>\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n<p>Check out the\u00a0<strong><a href=\"https:\/\/github.com\/Tencent-Hunyuan\/Hunyuan-MT\/blob\/main\/Hunyuan_MT_Technical_Report.pdf\">Paper<\/a>, <a href=\"https:\/\/github.com\/Tencent-Hunyuan\/Hunyuan-MT\/\">GitHub Page<\/a>,\u00a0and\u00a0<a href=\"https:\/\/huggingface.co\/collections\/tencent\/hunyuan-mt-68b42f76d473f82798882597\">Model on Hugging Face<\/a><em>.<\/em><\/strong>\u00a0All credit for this research goes to the researchers of this project. Feel free to check out our\u00a0<strong><mark><a href=\"https:\/\/github.com\/Marktechpost\/AI-Tutorial-Codes-Included\" target=\"_blank\" rel=\"noreferrer noopener\">GitHub Page for Tutorials, Codes and Notebooks<\/a><\/mark><\/strong>.\u00a0Also,\u00a0feel free to follow us on\u00a0<strong><a href=\"https:\/\/x.com\/intent\/follow?screen_name=marktechpost\" target=\"_blank\" rel=\"noreferrer noopener\"><mark>Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0<strong><a href=\"https:\/\/www.reddit.com\/r\/machinelearningnews\/\" target=\"_blank\" rel=\"noreferrer noopener\">100k+ ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0<strong><a href=\"https:\/\/www.aidevsignals.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">our Newsletter<\/a><\/strong>.<\/p>\n<p>The post <a href=\"https:\/\/www.marktechpost.com\/2025\/09\/02\/tencent-hunyuan-open-sources-hunyuan-mt-7b-and-hunyuan-mt-chimera-7b-a-state-of-the-art-multilingual-translation-models\/\">Tencent Hunyuan Open-Sources Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B: A State-of-the-Art Multilingual Translation Models<\/a> appeared first on <a href=\"https:\/\/www.marktechpost.com\/\">MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Introduction Tencent\u2019s 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\u2019s participation in the WMT2025 General Machine Translation shared task, where Hunyuan-MT-7B ranked first in 30 out of 31 language pairs. https:\/\/github.com\/Tencent-Hunyuan\/Hunyuan-MT\/blob\/main\/Hunyuan_MT_Technical_Report.pdf 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. https:\/\/github.com\/Tencent-Hunyuan\/Hunyuan-MT\/blob\/main\/Hunyuan_MT_Technical_Report.pdf Training Framework The models were trained using a five-stage framework designed for translation tasks: 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. 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. Supervised Fine-Tuning (SFT) Stage I: ~3M parallel pairs (Flores-200, WMT test sets, curated Mandarin\u2013minority data, synthetic pairs, instruction-tuning data). Stage II: ~268k high-quality pairs selected through automated scoring (CometKiwi, GEMBA) and manual verification. 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. 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\u21d4XX): 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\u21d4Minority 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\u201365% 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\u21d4Other and non-English\u21d4non-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 \u201c\u5c0f\u7ea2\u85af\u201d as the platform \u201cREDnote,\u201d unlike Google Translate\u2019s \u201csweet potatoes.\u201d Idioms: Interprets \u201cYou are killing me\u201d as \u201c\u4f60\u771f\u8981\u628a\u6211\u7b11\u6b7b\u4e86\u201d (expressing amusement), avoiding literal misinterpretation. Medical Terms: Translates \u201curic acid kidney stones\u201d 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\u2019s 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. Check out the\u00a0Paper, GitHub Page,\u00a0and\u00a0Model on Hugging Face.\u00a0All credit for this research goes to the researchers of this project. Feel free to check out our\u00a0GitHub Page for Tutorials, Codes and Notebooks.\u00a0Also,\u00a0feel free to follow us on\u00a0Twitter\u00a0and don\u2019t forget to join our\u00a0100k+ ML SubReddit\u00a0and Subscribe to\u00a0our Newsletter. The post Tencent Hunyuan Open-Sources Hunyuan-MT-7B and Hunyuan-MT-Chimera-7B: A State-of-the-Art Multilingual Translation Models appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":35794,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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NU","author_link":"https:\/\/youzum.net\/ja\/members\/adminnu\/"},"rttpg_comment":0,"rttpg_category":"<a href=\"https:\/\/youzum.net\/ja\/category\/ai-club\/\" rel=\"category tag\">AI<\/a> <a href=\"https:\/\/youzum.net\/ja\/category\/committee\/\" rel=\"category tag\">Committee<\/a> <a href=\"https:\/\/youzum.net\/ja\/category\/news\/\" rel=\"category tag\">News<\/a> <a href=\"https:\/\/youzum.net\/ja\/category\/uncategorized\/\" rel=\"category tag\">Uncategorized<\/a>","rttpg_excerpt":"Introduction Tencent\u2019s Hunyuan team has released Hunyuan-MT-7B (a translation model) and Hunyuan-MT-Chimera-7B (an ensemble model). 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