Transformers
GGUF
English
reasoning
preference_learning
nca
Inference Endpoints
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About

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

static quants are available at https://huggingface.co/mradermacher/Eurux-8x22b-nca-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 29.7 for the desperate
GGUF i1-IQ1_M 32.8 mostly desperate
GGUF i1-IQ2_XXS 38.0
GGUF i1-IQ2_XS 42.1
GGUF i1-IQ2_S 42.7
GGUF i1-IQ2_M 46.8
PART 1 PART 2 i1-Q2_K 52.2 IQ3_XXS probably better
PART 1 PART 2 i1-IQ3_XXS 55.0 lower quality
PART 1 PART 2 i1-IQ3_XS 58.3
PART 1 PART 2 i1-IQ3_S 61.6 beats Q3_K*
PART 1 PART 2 i1-Q3_K_S 61.6 IQ3_XS probably better
PART 1 PART 2 i1-IQ3_M 64.6
PART 1 PART 2 i1-Q3_K_M 67.9 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 72.7 IQ3_M probably better
PART 1 PART 2 i1-IQ4_XS 75.6
PART 1 PART 2 i1-Q4_0 80.0 fast, low quality
PART 1 PART 2 i1-Q4_K_S 80.6 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 85.7 fast, recommended
PART 1 PART 2 i1-Q5_K_S 97.1
PART 1 PART 2 PART 3 i1-Q5_K_M 100.1
PART 1 PART 2 PART 3 i1-Q6_K 115.6 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.

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GGUF
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Quantized from

Datasets used to train mradermacher/Eurux-8x22b-nca-i1-GGUF