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metadata
base_model: Kukedlc/Neural-4-Wino-7b
language:
  - en
library_name: transformers
quantized_by: mradermacher
tags:
  - merge
  - mergekit
  - lazymergekit
  - Kukedlc/NeuralFusion-7b-Dare-Ties
  - paulml/OmniBeagleSquaredMBX-v3-7B-v2
  - macadeliccc/MBX-7B-v3-DPO
  - Kukedlc/Fasciculus-Arcuatus-7B-slerp
  - liminerity/Neurotic-Jomainotrik-7b-slerp

About

static quants of https://huggingface.co/Kukedlc/Neural-4-Wino-7b

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

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.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link Type Size/GB Notes
GGUF Q2_K 3.0
GGUF IQ3_XS 3.3
GGUF Q3_K_S 3.4
GGUF IQ3_S 3.4 beats Q3_K*
GGUF IQ3_M 3.5
GGUF Q3_K_M 3.8 lower quality
GGUF Q3_K_L 4.1
GGUF IQ4_XS 4.2
GGUF Q4_0 4.4 fast, low quality
GGUF Q4_K_S 4.4 fast, recommended
GGUF IQ4_NL 4.4 prefer IQ4_XS
GGUF Q4_K_M 4.6 fast, recommended
GGUF Q5_K_S 5.3
GGUF Q5_K_M 5.4
GGUF Q6_K 6.2 very good quality
GGUF Q8_0 7.9 fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

image.png

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.