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AI 모델 84759

• 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.

• neutts-nano-spanish↗

  neuphonic

• TinyLlama-1.1B-Chat-v1.0-kvcache-fp8-tensor↗

  nm-testing

• tiny-random-Idefics3ForConditionalGeneration↗

  optimum-intel-internal-testing

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• tiny-random-xlm-roberta↗

  optimum-intel-internal-testing

• mark5↗

  philip324mark

• Qwen3-VL-2B-Instruct-abliterated-v1-GGUF↗

  prithivMLmods

• Qwen3-VL-Embedding-8B-W8A16↗

  QuaduxIT

• Fireball-Alpaca-Llama-3.1-8B-Philos-DPO-200-GGUF↗

  QuantFactory