GK-inv-MoE-0.1-GGUF / README.md
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metadata
base_model: powermove72/GK-inv-MoE-0.1
language:
  - en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
  - moe
  - frankenmoe
  - merge
  - mergekit
  - lazymergekit
  - GritLM/GritLM-7B
  - argilla/notus-7b-v1

About

static quants of https://huggingface.co/powermove72/GK-inv-MoE-0.1

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 4.9
GGUF IQ3_XS 5.4
GGUF Q3_K_S 5.7
GGUF IQ3_S 5.7 beats Q3_K*
GGUF IQ3_M 5.8
GGUF Q3_K_M 6.3 lower quality
GGUF Q3_K_L 6.8
GGUF IQ4_XS 7.1
GGUF Q4_K_S 7.4 fast, recommended
GGUF Q4_K_M 7.9 fast, recommended
GGUF Q5_K_S 9.0
GGUF Q5_K_M 9.2
GGUF Q6_K 10.7 very good quality
GGUF Q8_0 13.8 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.