This is a simplified guide to an AI model called HY-MT1.5-1.8B maintained by tencent. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter. This is a simplified guide to an AI model called HY-MT1.5-1.8B HY-MT1.5-1.8B maintained by tencent tencent . If you like these kinds of analysis, join AIModels.fyi AIModels.fyi or follow us on Twitter Twitter . Model overview HY-MT1.5-1.8B is a compact translation model from Tencent that delivers translation performance comparable to much larger models. The 1.8B parameter model achieves industry-leading results among models of the same size, surpassing most commercial translation APIs. This compact design makes it fundamentally different from its larger 7B counterpart, the Hunyuan-MT-7B, which won first place in 30 out of 31 language categories in the WMT25 competition. Despite having less than one-third the parameters of the 7B version, the 1.8B model maintains comparable translation quality while prioritizing speed and efficiency. The model supports mutual translation across 33 languages and includes 5 ethnic and dialect variations. HY-MT1.5-1.8B Tencent Hunyuan-MT-7B Model inputs and outputs HY-MT1.5-1.8B takes source text in one language and outputs translated text in a target language. The model accepts flexible input formats depending on the translation task, from simple text to complex scenarios with terminology requirements or contextual information. HY-MT1.5-1.8B Inputs Source text in any of 33 supported languagesTarget language specified as a language code or nameOptional terminology pairs for controlled vocabulary translationOptional context to guide translation interpretationOptional formatting markers to preserve document structure Source text in any of 33 supported languages Source text Target language specified as a language code or name Target language Optional terminology pairs for controlled vocabulary translation Optional terminology pairs Optional context to guide translation interpretation Optional context Optional formatting markers to preserve document structure Optional formatting markers Outputs Translated text in the target languagePreserved formatting when formatted translation templates are usedConsistent terminology when terminology intervention is appliedContext-aware translations reflecting provided contextual information Translated text in the target language Translated text Preserved formatting when formatted translation templates are used Preserved formatting Consistent terminology when terminology intervention is applied Consistent terminology Context-aware translations reflecting provided contextual information Context-aware translations Capabilities The model handles diverse translation scenarios with specialized prompt templates. For Chinese to other language translations, users provide the target language followed by source text. For non-Chinese language pairs, the model accepts instructions without additional explanation. The terminology intervention feature allows users to specify how particular terms should translate, maintaining consistency across documents. Contextual translation lets the model consider surrounding information for better interpretation, and formatted translation preserves document structure and layout during translation. After quantization using FP8 or GPTQ-Int4 compression, the model deploys on edge devices and supports real-time translation in production environments. What can I use it for? This model suits real-time translation applications on resource-constrained devices, content localization for global audiences, multilingual customer support systems, and document translation with specialized terminology. Organizations can integrate it into mobile applications, IoT devices, or edge servers where traditional large translation models would consume excessive resources. The terminology intervention and contextual translation features serve document-heavy industries like legal, medical, and technical translation. Companies can monetize translation services through APIs, reduce infrastructure costs by running models locally, or embed translation capabilities into consumer products without relying on expensive commercial translation services. Things to try Experiment with the Hunyuan-MT-Chimera-7B ensemble model if you need maximum translation quality, as it combines multiple translation approaches for superior results. Test terminology intervention with domain-specific content to maintain consistent technical vocabulary across translations. Try the quantized variants (FP8 or GPTQ-Int4) on edge devices to understand the accuracy trade-offs when deploying at extreme scale. Compare results between the 1.8B and 7B models on the same content to identify tasks where the compact model reaches your quality threshold, helping optimize deployment costs versus quality requirements. Hunyuan-MT-Chimera-7B

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