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About

weighted/imatrix quants of https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO

static quants are available at https://huggingface.co/mradermacher/Nous-Hermes-2-Mixtral-8x7B-DPO-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 9.9 for the desperate
GGUF i1-IQ1_M 10.9 mostly desperate
GGUF i1-IQ2_XXS 12.7
GGUF i1-IQ2_XS 14.0
GGUF i1-IQ2_S 14.2
GGUF i1-IQ2_M 15.6
GGUF i1-Q2_K 17.4 IQ3_XXS probably better
GGUF i1-IQ3_XXS 18.3 lower quality
GGUF i1-IQ3_XS 19.5
GGUF i1-IQ3_S 20.5 beats Q3_K*
GGUF i1-Q3_K_S 20.5 IQ3_XS probably better
GGUF i1-IQ3_M 21.5
GGUF i1-Q3_K_M 22.6 IQ3_S probably better
GGUF i1-Q3_K_L 24.3 IQ3_M probably better
GGUF i1-IQ4_XS 25.2
GGUF i1-Q4_0 26.7 fast, low quality
GGUF i1-Q4_K_S 26.8 optimal size/speed/quality
GGUF i1-Q4_K_M 28.5 fast, recommended
GGUF i1-Q5_K_S 32.3
GGUF i1-Q5_K_M 33.3
GGUF i1-Q6_K 38.5 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. Additional thanks to @nicoboss for giving me access to his hardware for calculating the imatrix for these quants.

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Quantized from

Dataset used to train mradermacher/Nous-Hermes-2-Mixtral-8x7B-DPO-i1-GGUF