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metadata
base_model: CultriX/NeuralMona_MoE-4x7B
language:
  - en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
  - moe
  - frankenmoe
  - merge
  - mergekit
  - lazymergekit
  - CultriX/MonaTrix-v4
  - mlabonne/OmniTruthyBeagle-7B-v0
  - CultriX/MoNeuTrix-7B-v1
  - paulml/OmniBeagleSquaredMBX-v3-7B

About

static quants of https://huggingface.co/CultriX/NeuralMona_MoE-4x7B

weighted/imatrix quants are available at https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF

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 9.1
GGUF IQ3_XS 10.1
GGUF Q3_K_S 10.7
GGUF IQ3_S 10.7 beats Q3_K*
GGUF IQ3_M 10.9
GGUF Q3_K_M 11.8 lower quality
GGUF Q3_K_L 12.8
GGUF IQ4_XS 13.3
GGUF Q4_0 13.9 fast, low quality
GGUF Q4_K_S 14.0 fast, recommended
GGUF IQ4_NL 14.0 prefer IQ4_XS
GGUF Q4_K_M 14.9 fast, recommended
GGUF Q5_K_S 16.9
GGUF Q5_K_M 17.4
GGUF Q6_K 20.1 very good quality
GGUF Q8_0 25.9 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.