## 📣 Latest News
- [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 also have made an [Android Demo](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF/resolve/main/Hy-MT-demo.apk?download=true) 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]](https://arxiv.org/abs/2601.07892) | [[Code]](https://github.com/Tencent/AngelSlim/tree/sherry/Sherry)
For more detailed information, please refer to [[AngelSlim]](https://github.com/Tencent/AngelSlim) and [[HY-MT]](https://github.com/Tencent-Hunyuan/HY-MT)
## 🌟 Hy-MT1.5-1.8B-2bit Key Features
- **World-Class Translation Quality** Hy-MT1.5-1.8B-2bit 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 Technical Report](https://arxiv.org/abs/2512.24092).
- **Ultra-Compact 2-bit Quantization** Hy-MT1.5-1.8B-2bit employs industry-leading Stretched Elastic Quantization (SEQ) to quantize model weights to `{-1.5, -0.5, 0.5, 1.5}`, combined with quantization-aware distillation. This compresses the original 3.3GB FP16 model down to just **574MB** while maintaining near-lossless translation quality that surpasses models hundreds of GBs in size. The quantization details are described in the [AngelSlim Technical Report](https://arxiv.org/abs/2602.21233).
- **On-Device Deployment** Optimized for Arm SME2-capable mobile devices (e.g., Apple M4, vivo x300), the 2-bit model enables fast, fully offline translation directly on your phone, no internet connection required. Your data never leaves the device, ensuring complete privacy.
## 📈 Translation Benchmarks
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.
## ⚡ Speed Demo
Speed comparison of the 2-bit model on SME2 and Neon kernels:
Speed comparison of the 2-bit model on SME2 and Neon kernels.
## 📱 Demo
We provide a ready-to-use Android demo APK for offline translation. The app 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
Demo device: Snapdragon 865, 8GB RAM.
### Background Word Extraction Mode
Demo device: Snapdragon 7+ Gen 2, 16GB RAM.
## 💻 Deployment
**❕❕ This gguf depends on Q2_0c kernel, which is released at [PR #19357](https://github.com/ggml-org/llama.cpp/pull/19357).**
### Clone llama.cpp
```bash
git clone https://github.com/ggml-org/llama.cpp.git
```
### Enter the llama.cpp folder
```bash
cd llama.cpp
```
### Fetch and check out the PR branch
```bash
git fetch origin pull/19357/head:pr-19357-sme2-int2
git checkout pr-19357-sme2-int2
```
#### Build llama.cpp
```bash
pip install -r requirements.txt
cmake -B build
cmake --build build --config Release
```
### Download the HF model
```bash
pip install huggingface_hub
hf download AngelSlim/Hy-MT1.5-1.8B-2bit \
--local-dir model_zoo/Hy-MT1.5-1.8B-2bit
```
### Convert HF → bf16 GGUF
```bash
python convert_hf_to_gguf.py model_zoo/Hy-MT1.5-1.8B-2bit \
--outfile model_zoo/Hy-MT1.5-1.8B-2bit-qdq.gguf \
--outtype bf16
```
### Quantize bf16 → Q2_0c
```bash
./build/bin/llama-quantize \
model_zoo/Hy-MT1.5-1.8B-2bit-qdq.gguf\
model_zoo/Hy-MT1.5-1.8B-Q2_0c.gguf \
q2_0c
```
### Run a completion example
The prompt format can be viewed at [HY-MT1.5-1.8B](https://huggingface.co/tencent/HY-MT1.5-1.8B)
```bash
./build/bin/llama-completion \
--model model_zoo/Hy-MT1.5-1.8B-Q2_0c.gguf \
-p "Translate the following segment into Chinese, without additional explanation. Hello " \
--jinja \
-ngl 0 \
-n 64 -st
```
### Run the llama.cpp benchmark
```bash
./build/bin/llama-bench -m model_zoo/Hy-MT1.5-1.8B-Q2_0c.gguf -ngl 0
```
## 📥 Download Links
- 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
- 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
- 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
- AngelSlim Technical Report: https://arxiv.org/abs/2602.21233
- Sherry Paper: https://arxiv.org/abs/2601.07892
## 📝 License
The code for this project is open-sourced under the [License for AngelSlim](LICENSE).
## 🔗 Citation
```bibtex
@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](https://github.com/Tencent/AngelSlim/issues) or join our [WeChat discussion group](https://github.com/Tencent/AngelSlim/blob/main/docs/source/assets/angel_slim_wechat.png?raw=true).
