HY-1.8B-2Bit-GGUF
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Explore AI models 13
Hy-MT1.5-1.8B-1.25bit-GGUF
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View details ↗Hy-MT2-1.8B-1.25Bit-GGUF
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View details ↗Hy3-GGUF
AngelSlimDedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit. 📖 Documentation    |   🤗 Hugging Face    |   🤖 ModelScope    |   💬 WeChat Low-bit quantized **Hy3 (hy_v3)** GGUFs for llama.cpp — ready to run out of the box, with MTP self-speculative decoding. This repo ships the quantized models, plus the mixed-precision recipes to build your own from a calibration set.
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View details ↗Hy3-GPTQ-Int4
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View details ↗Hy4-preview-GGUF
AngelSlim**Neither file runs on stock llama.cpp.** The `hyv4` architecture is not upstream. Apply the patches in `hy4-preview-patch/` **`Hy4-preview-Q4_K_M.gguf`** — a conventional Q4_K_M. Most tensors are Q4_K; `ffn_down_exps` gets Q6_K on 37 layers via llama.cpp's own logic. Use this unless you are memory-constrained. **`Hy4-preview-UD-IQ1_M.gguf`** - mixed precision with UD-IQ1_M strategy at ~2.44 bpw, roughly **half the size** for the same model. The routed-expert `gate`/`up` projections run at 1.75 bpw (IQ1_M) and 2.0625 bpw (IQ2_XXS).
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View details ↗Qwen3-1.7B_eagle3
AngelSlimDedicated to building a more intuitive, comprehensive, and efficient LLMs compression toolkit. 📖 Documentation    |   🤗 Hugging Face    |   🤖 ModelScope    |   💬 WeChat - [26/01/13] We have released v0.3. We support the training and deployment of Eagle3 for all-scale LLMs/VLMs/Audio models, as detailed in the guidance documentation. And We released **Sherry**, the hardware-efficient 1.25 bit quantization algorithm [Paper Comming soon] | [[Code]](https://github.com/Tencent/AngelSlim/tree/sherry/Sherry)🔥🔥🔥 - [25/11/05] We have released v0.2. Quantization support for new models, such as `GLM-4.6`, `Qwen3-VL` and `Qwen3-Omni`, open-sources the Eagle3 speculative decoding training framework, and updates the Diffusion model quantization tools. - [25/09/30] We have released **SpecExit**, the reasoning early-exit algorithm: [[Paper]](http://arxiv.org/abs/2509.24248) | [[Docs]](https://angelslim.readthedocs.io/zh-cn/latest/features/speculative_decoding/spec_exit.html) | [[vLLM Code]](https://github.com/vllm-project/vllm/pull/27192) - [25/09/26] We have released **TEQUILA**, the ternary quantization algorithm [[Paper]](https://arxiv.org/abs/2509.23809) | [[Code]](https://github.com/Tencent/AngelSlim/tree/tequila/TernaryQuant) - [25/09/24] We now support the PTQ quantization of NVFP4 for the Qwen3 series models. We also opensource Qwen3-32B-NVFP4 and Qwen3-235B-A22B-NVFP4 weights.
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View details ↗Qwen3-14B_eagle3
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View details ↗Qwen3-32B_eagle3
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View details ↗Qwen3-4B_eagle3
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View details ↗Qwen3-8B_eagle3
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View details ↗Qwen3-a3B_eagle3
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