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Hy-MT1.5-1.8B translation quality scores. Source: HY-MT1.5 Technical Report

• [26/05/08] We have released STQ1_0 kernel for 1.25-bit model and given a PR to llama.cpp PR #22836 ! If you have any questions or suggestions for STQ_0, welcome to comment under the PR !🔥🔥🔥
• [26/04/29] We have released Hy-MT1.5-1.8B-2bit (574MB) and Hy-MT1.5-1.8B-1.25bit (440MB), on-device translation models supporting 33 languages, with both weights and GGUF formats available. We have also made an Android Demo for you to try out. We invite you to give it a spin! 🔥🔥🔥
• [26/02/09] We have released HY-1.8B-2Bit, 2-bit on-device large language model.
• [26/01/13] We have released v0.3. We support the training and deployment of Eagle3 for all-scale LLMs/VLMs/Audio models. And we released Sherry, the hardware-efficient 1.25-bit quantization algorithm [Paper] | [Code]

For more detailed information, please refer to [AngelSlim] and [HY-MT]

• World-Class Translation Quality Hy-MT1.5-1.8B-1.25bit is built upon the Hy-MT1.5-1.8B foundation model, a specialized translation model developed by Tencent Hunyuan Team through a holistic multi-stage training pipeline integrating MT-oriented pre-training, supervised fine-tuning, on-policy distillation, and reinforcement learning. The base model natively supports 33 languages, 5 dialects/minority languages, and 1,056 translation directions. With only 1.8B parameters, it comprehensively outperforms much larger open-source models (e.g., Tower-Plus-72B, Qwen3-32B) and mainstream commercial translation APIs (e.g., Microsoft Translator, Doubao Translator). For full details, please refer to the HY-MT1.5-1.8B and HY-MT1.5 Technical Report.

• Sherry: Extreme 1.25-bit Quantization This model employs Sherry (accepted at ACL 2026), a hardware-efficient ternary quantization framework. Sherry introduces a 3:4 fine-grained sparsity strategy: for every 4 model weights, the 3 most important are stored in 1-bit ({-1, +1}), while the remaining 1 is zeroed out. This packs 4 weights into just 5 bits, achieving an effective 1.25-bit width with power-of-two alignment, compressing the original 3.3GB FP16 model to just 440MB, with minimal accuracy loss.

Sherry fine-grained sparsity: for every 4 weights, the 3 most important are stored in 1-bit, and the remaining 1 is zeroed out.

• On-Device Deployment for the Most Phones Paired with our custom STQ kernel designed specifically for mobile CPUs, the 1.25-bit model achieves perfect SIMD instruction set alignment. This means even ordinary phones with limited memory can run high-quality offline translation smoothly. No internet connection required, and your data never leaves the device.

Performance comparison of different model sizes on the Flores-200 Chinese-Foreign mutual translation benchmark:

Performance of different model sizes on the Flores-200 Chinese-Foreign mutual translation benchmark.

FP16 (8x speed) vs. 1.25-bit speed comparison. Demo device: Snapdragon 888, 8GB RAM:

Demo device: Snapdragon 888, 8GB RAM.

We provide a ready-to-use Android demo for offline translation. The demo features a background word extraction mode that works across any app on your phone — browse emails, webpages, or chat messages and get instant translations without switching apps. No network required, no data collection, one-time download for permanent use.

Download Demo:

https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit-GGUF/resolve/main/Hy-MT-demo.apk

Demo device: Snapdragon 865, 8GB RAM.

Demo device: Snapdragon 7+ Gen 2, 16GB RAM.

git clone https://github.com/ggml-org/llama.cpp.git

cd llama.cpp

git fetch origin pull/22836/head:pr-22836-stq_0
git checkout pr-22836-stq_0

pip install -r requirements.txt
cmake -B build
cmake --build build --config Release

pip install huggingface_hub
huggingface-cli download AngelSlim/Hy-MT1.5-1.8B-1.25bit \
    --local-dir model_zoo/Hy-MT1.5-1.8B-1.25bit

python convert_hf_to_gguf.py model_zoo/Hy-MT1.5-1.8B-1.25bit \
    --outfile model_zoo/Hy-MT1.5-1.8B-bf16.gguf \
    --outtype bf16

./build/bin/llama-quantize \
    model_zoo/Hy-MT1.5-1.8B-bf16.gguf \
    model_zoo/Hy-MT1.5-1.8B-STQ1_0.gguf \
    STQ1_0

The prompt format can be viewed at HY-MT1.5-1.8B

./build/bin/llama-completion \
  --model model_zoo/Hy-MT1.5-1.8B-STQ1_0.gguf \
  -p "Translate the following segment into Chinese, without additional explanation. Hello " \
  --jinja \
  -ngl 0 \
  -n 64 -st

./build/bin/llama-bench -m model_zoo/Hy-MT1.5-1.8B-STQ1_0.gguf -ngl 0

• 1.25-bit model weights: https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit
• 1.25-bit model GGUF: https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit-GGUF
• 2-bit model weights: https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-2bit
• 2-bit model GGUF: https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
• Demo: https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit-GGUF/resolve/main/Hy-MT-demo.apk

• HY-MT1.5 Technical Report: https://arxiv.org/abs/2512.24092
• Sherry Paper (ACL 2026): https://arxiv.org/abs/2601.07892
• AngelSlim Technical Report: https://arxiv.org/abs/2602.21233

The code for this project is open-sourced under the License for AngelSlim.

@misc{huang2026sherry,
      title={Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification}, 
      author={Hong Huang and Decheng Wu and Qiangqiang Hu and Guanghua Yu and Jinhai Yang and Jianchen Zhu and Xue Liu and Dapeng Wu},
      year={2026},
      eprint={2601.07892},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2601.07892}, 
}

@article{angelslim2026,
  title={AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression},
  author={Hunyuan AI Infra Team},
  journal={arXiv preprint arXiv:2602.21233},
  year={2026}
}

@misc{zheng2025hymt,
      title={HY-MT1.5 Technical Report}, 
      author={Mao Zheng and Zheng Li and Tao Chen and Mingyang Song and Di Wang},
      year={2025},
      eprint={2512.24092},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2512.24092}, 
}

• AngelSlim is continuously iterating and new features will be released soon. If you have any questions or suggestions, please open an issue on GitHub Issues or join our WeChat discussion group.