Qwen2.5-7B-Instruct-Uncensored_GGUF
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Qwen2.5-VL-7B-Abliterated-Caption-it_GGUF
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View details ↗Qwen3.5-4B-Uncensored-HauhauCS-Aggressive
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View details ↗Z-images_models
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View details ↗Qwen3.8-27B-OBLITERATED
andyman5002基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ecce-vectors
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View details ↗Gemma-4-E4B-Nightcap-GGUF
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View details ↗anemll-forge-qwen3.8-27B
anemll基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wan2.2-I2V-A14B-mlx-q8
Anes1032基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wan2.2-TI2V-5B-mlx-q8
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View details ↗awesome-align-with-co
aneuraz`awesome-align` is a tool that can extract word alignments from multilingual BERT (mBERT) Demo and allows you to fine-tune mBERT on parallel corpora for better alignment quality (see our paper for more details).
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View details ↗bert-for-patents
anfericoBERT for Patents is a model trained by Google on 100M+ patents (not just US patents). It is based on BERT LARGE . If you want to learn more about the model, check out the blog post, white paper and GitHub page containing the original TensorFlow checkpoint.
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View details ↗RaTE-NER-Deberta
AngelakekeThis model is a fine-tuned version of DeBERTa on the RaTE-NER dataset. This model is trained to serve the RaTEScore metric, if you are interested in our pipeline, please refer to our paper and Github. This model also can be used to extract **Abnormality, Non-Abnormality, Anatomy, Disease, Non-Disease** in medical radiology reports.
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View details ↗HY-1.8B-2Bit-GGUF
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View details ↗Hy-MT1.5-1.8B-1.25bit-GGUF
AngelSlim基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Hy-MT2-1.8B-1.25Bit-GGUF
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View details ↗Hy-MT2-1.8B-2Bit-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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