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AI 模型 84757

• PsyMedLewdPass-GGUF↗

  mradermacher

• QiMing-Sales-20B-MXFP4-i1-GGUF↗

  mradermacher

• Quill-v1-GGUF↗

  mradermacher

• Qwen-32B-VL-combined-noCOT-GGUF↗

  mradermacher

• Qwen2.5-VL-32B-Instruct-i1-GGUF↗

  mradermacher

  If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

• R2EGym-32B-Agent-i1-GGUF↗

  mradermacher

• RYS-Llama-3.1-8B-Instruct-i1-GGUF↗

  mradermacher

• SmaliLLM-Qwen2.5-Coder-0.5B-Finetuned-i1-GGUF↗

  mradermacher

• WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b-i1-GGUF↗

  mradermacher

  weighted/imatrix quants of https://huggingface.co/Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b static quants are available at https://huggingface.co/mradermacher/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

• GELab-Zero-4B-preview-GGUF↗

  Mungert

  I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides. In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here: 👉 Layer bumping with llama.cpp While this does increase model file size, it significantly improves precision for a given quantization level.