📣 Latest News
- [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.
- [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]
🌟 Hy-MT1.5-1.8B-1.25bit Key Features
-
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.
- 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.
📈 Translation Benchmarks
Performance comparison of different model sizes on the Flores-200 Chinese-Foreign mutual translation benchmark:
⚡ Speed Demo
FP16 (8x speed) vs. 1.25-bit speed comparison. Demo device: Snapdragon 888, 8GB RAM:
📱 Demo
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
Translation Demo
Background Word Extraction Mode
❕ Usage
Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp.git
Enter the llama.cpp folder
cd llama.cpp
Fetch and check out the PR branch
git fetch origin pull/22836/head:pr-22836-stq_0
git checkout pr-22836-stq_0
Build llama.cpp
pip install -r requirements.txt
cmake -B build
cmake --build build --config Release
Download the HF model
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
Convert HF → bf16 GGUF
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
Quantize bf16 → STQ1_0
./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
Run a completion example
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
Run the llama.cpp benchmark
./build/bin/llama-bench -m model_zoo/Hy-MT1.5-1.8B-STQ1_0.gguf -ngl 0
📥 Download Links
- 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
📄 Technical Reports
- 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
📝 License
The code for this project is open-sourced under the License for AngelSlim.
🔗 Citation
@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},
}
💬 Technical Discussion
- 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.
