Transformers
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reasoning
preference_learning
nca
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About

static quants of https://huggingface.co/openbmb/Eurux-8x22b-nca

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Eurux-8x22b-nca-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
PART 1 PART 2 Q2_K 52.2
PART 1 PART 2 IQ3_XS 58.3
PART 1 PART 2 IQ3_S 61.6 beats Q3_K*
PART 1 PART 2 Q3_K_S 61.6
PART 1 PART 2 IQ3_M 64.6
PART 1 PART 2 Q3_K_M 67.9 lower quality
PART 1 PART 2 Q3_K_L 72.7
PART 1 PART 2 IQ4_XS 76.5
PART 1 PART 2 Q4_K_S 80.6 fast, recommended
PART 1 PART 2 Q4_K_M 85.7 fast, recommended
PART 1 PART 2 Q5_K_S 97.1
PART 1 PART 2 PART 3 Q5_K_M 100.1
PART 1 PART 2 PART 3 Q6_K 115.6 very good quality
PART 1 PART 2 PART 3 PART 4 Q8_0 149.5 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.

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Datasets used to train mradermacher/Eurux-8x22b-nca-GGUF