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    HY-MT

    HY-MT

    Hunyuan Translation Model Version 1.5

    HY-MT (Hunyuan Translation) is a high-quality multilingual machine translation model suite developed to support mutual translation across dozens of languages with strong performance even at smaller model scales. It ships with both an 1.8 B parameter model and a larger 7 B model, the latter optimized not only for direct translation but also for formatted and contextualized output, allowing better handling of terminology and mixed-language content.
    Downloads: 3 This Week
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    Sockeye

    Sockeye

    Sequence-to-sequence framework, focused on Neural Machine Translation

    Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial. If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com. Developers may be interested in our developer guidelines. ...
    Downloads: 0 This Week
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  • 3
    Phramer - An Open-Source Statistical Phrase-Based Machine Translation Decoder ||| Project web page: http://www.phramer.org
    Downloads: 0 This Week
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  • 4
    Hunyuan-MT-7B

    Hunyuan-MT-7B

    Tencent’s 36-language state-of-the-art translation model

    Hunyuan-MT-7B is a large-scale multilingual translation model developed by Tencent, designed to deliver state-of-the-art translation quality across 36 languages, including several Chinese ethnic minority languages. It forms part of the Hunyuan Translation Model family, alongside Hunyuan-MT-Chimera, which ensembles outputs for even higher accuracy.
    Downloads: 0 This Week
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    Qwen2-7B-Instruct

    Qwen2-7B-Instruct

    Instruction-tuned 7B language model for chat and complex tasks

    ...The model was pretrained on a large-scale dataset and aligned via supervised fine-tuning and direct preference optimization. It shows strong performance across benchmarks such as MMLU, MT-Bench, GSM8K, and Humaneval, often surpassing similarly sized open-source models. Designed for conversational use, it integrates with Hugging Face Transformers and supports long-context applications via YARN and vLLM for efficient deployment.
    Downloads: 0 This Week
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