diff --git a/README.md b/README.md
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+++ b/README.md
@@ -22,6 +22,7 @@ A more accessible, comprehensive, and efficient toolkit for large model compress
 </p>
 
 ## 📣Latest News
+- [26/04/29] We have released 2-bit and 1.25-bit versions of Tencent Hy-MT1.5-1.8B Translation Model: [Hy-MT1.5-1.8B-2bit](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-2bit) and [Hy-MT1.5-1.8B-1.25bit](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit). Additionally, we have make an [offline translation demo](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit/blob/main/Hy-MT-demo.apk) for you to try out. We invite you to give it a spin! 🔥🔥🔥
 - [26/04/23] We now support FP8-Static quantization for **Hy3-preview** (MoE A20B).
 - [26/03/25] We have released **DAQ**, the quantization algorithm that preserves the knowledge acquired while the update of parameters is relatively small during post-training training.[[Paper]](https://arxiv.org/abs/2603.22324) | [[Docs]](docs/source/features/quantization/daq.md)
 - [26/02/09] We have released HY-1.8B-2Bit, 2bit on-device large language model,[[Huggingface]](https://huggingface.co/AngelSlim/HY-1.8B-2Bit).
diff --git a/README_cn.md b/README_cn.md
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@@ -22,6 +22,7 @@
 </p>
 
 ## 📣最新进展
+- [26/04/29] 我们发布了 2bit 与 1.25bit 腾讯混元翻译模型 [Hy-MT1.5-1.8B-2bit](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-2bit), [Hy-MT1.5-1.8B-1.25bit](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit)。并且还制作了 [离线翻译体验 Demo](https://huggingface.co/AngelSlim/Hy-MT1.5-1.8B-1.25bit/blob/main/Hy-MT-demo.apk)。 欢迎体验 🔥🔥🔥
 - [26/04/23] 我们支持了 **Hy3-preview**（MoE A20B）模型的 FP8-Static 量化。
 - [26/03/25] 我们发布了量化算法DAQ，该方法在后训练参数更新较小时，可保留量化后模型能力 [[论文]](https://arxiv.org/abs/2603.22324) | [[文档]](docs/source/features/quantization/daq.md)
 - [26/02/09] 我们发布了 HY-1.8B-2Bit, 2比特端侧大模型, 模型可见[[Huggingface]](https://huggingface.co/AngelSlim/HY-1.8B-2Bit).
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@@ -146,6 +146,7 @@ models/deepseek/deepseek_quant
 models/qwen/qwen_quant
 models/qwenvl/qwenvl_quant
 models/qwen3_omni/qwen3_omni_quant
+models/Hy-MT1.5/hy-mt1.5
 :::
 
 
diff --git a/docs/source/models/Hy-MT1.5/hy-mt1.5.md b/docs/source/models/Hy-MT1.5/hy-mt1.5.md
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+# Hy-MT1.5 量化
+
+
+:::{figure} /assets/HYMT1.5/model_scores.png
+:align: center
+:alt: model_scores
+:width: 80%
+
+Hy-MT1.5-1.8B translation quality scores. Source: <a href="https://arxiv.org/abs/2512.24092">HY-MT1.5 Technical Report
+:::
+
+## 🌟 Key Features
+
+### World-Class Translation Quality
+
+Both Hy-MT1.5-1.8B-1.25bit and Hy-MT1.5-1.8B-2bit are 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).
+
+
+### Sherry: Extreme 1.25-bit Quantization (440MB)
+
+The 1.25-bit model employs [**Sherry**](https://arxiv.org/abs/2601.07892) (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.
+
+:::{figure} /assets/HYMT1.5/Sherry.png
+:align: center
+:alt: Sherry
+:width: 80%
+
+Sherry fine-grained sparsity: for every 4 weights, the 3 most important are stored in 1-bit, and the remaining 1 is zeroed out.
+:::
+
+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.
+
+### Ultra-Compact 2-bit Quantization (574MB)
+
+The 2-bit model 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).
+
+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:
+
+:::{figure} /assets/HYMT1.5/flores_model_size.png
+:align: center
+:alt: flores_model_size
+:width: 80%
+
+Performance of different model sizes on the Flores-200 Chinese-Foreign mutual translation benchmark.
+:::
+
+## ⚡ Speed Demos
+
+### 1.25-bit: FP16 (8x speed) vs. 1.25-bit
+
+:::{figure} /assets/HYMT1.5/fp16vs1.25bit.gif
+:align: center
+:alt: fp16_vs_1.25bit
+:width: 60%
+
+Demo device: Snapdragon 888, 8GB RAM.
+:::
+
+### 2-bit: SME2 vs. Neon Kernels
+
+:::{figure} /assets/HYMT1.5/sme2_2bit.gif
+:align: center
+:alt: sme2_2bit_speed
+:width: 60%
+
+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
+
+:::{figure} /assets/HYMT1.5/app_demo.gif
+:align: center
+:alt: app_demo
+:width: 40%
+
+Demo device: Snapdragon 865, 8GB RAM.
+:::
+
+### Background Word Extraction Mode
+
+:::{figure} /assets/HYMT1.5/demo2.gif
+:align: center
+:alt: demo2
+:width: 40%
+
+Demo device: Snapdragon 7+ Gen 2, 16GB RAM.
+:::
+
+## 💻 Deployment
+Our llama.cpp kernel (including STQ kernel) is coming soon. 
+
+## 📥 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](LICENSE).
+
+## 🔗 Citation
+
+```bibtex
+@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](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).