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metadata
base_model: Severian/Nexus-4x7B-IKM-MLX
datasets:
  - Severian/Internal-Knowledge-Map
language:
  - en
library_name: transformers
license: mit
quantized_by: mradermacher

About

weighted/imatrix quants of https://huggingface.co/Severian/Nexus-4x7B-IKM-MLX

static quants are available at https://huggingface.co/mradermacher/Nexus-4x7B-IKM-MLX-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 i1-IQ1_S 5.3 for the desperate
GGUF i1-IQ2_XXS 6.7
GGUF i1-IQ2_XS 7.4
GGUF i1-IQ2_S 7.6
GGUF i1-IQ2_M 8.3
GGUF i1-Q2_K 9.1 IQ3_XXS probably better
GGUF i1-IQ3_XXS 9.6 lower quality
GGUF i1-IQ3_XS 10.1
GGUF i1-Q3_K_S 10.7 IQ3_XS probably better
GGUF i1-IQ3_S 10.7 beats Q3_K*
GGUF i1-IQ3_M 10.9
GGUF i1-Q3_K_M 11.8 IQ3_S probably better
GGUF i1-Q3_K_L 12.8 IQ3_M probably better
GGUF i1-Q4_K_S 14.0 optimal size/speed/quality
GGUF i1-Q4_K_M 14.9 fast, recommended
GGUF i1-Q5_K_S 16.9
GGUF i1-Q5_K_M 17.4
GGUF i1-Q6_K 20.1 practically like static Q6_K

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.