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About

weighted/imatrix quants of https://huggingface.co/leafspark/Mistral-Large-218B-Instruct

static quants are available at https://huggingface.co/mradermacher/Mistral-Large-218B-Instruct-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 46.1 for the desperate
PART 1 PART 2 i1-IQ1_M 50.4 mostly desperate
PART 1 PART 2 i1-IQ2_XXS 57.6
PART 1 PART 2 i1-IQ2_XS 64.1
PART 1 PART 2 i1-IQ2_S 68.2
PART 1 PART 2 i1-IQ2_M 74.0
PART 1 PART 2 i1-Q2_K 80.4 IQ3_XXS probably better
PART 1 PART 2 i1-IQ3_XXS 83.6 lower quality
PART 1 PART 2 i1-IQ3_XS 89.2
PART 1 PART 2 i1-Q3_K_S 94.0 IQ3_XS probably better
PART 1 PART 2 i1-IQ3_S 94.3 beats Q3_K*
PART 1 PART 2 i1-IQ3_M 98.3
PART 1 PART 2 PART 3 i1-Q3_K_M 105.2 IQ3_S probably better
PART 1 PART 2 PART 3 i1-Q3_K_L 114.9 IQ3_M probably better
PART 1 PART 2 PART 3 i1-IQ4_XS 116.4
PART 1 PART 2 PART 3 i1-Q4_0 123.3 fast, low quality
PART 1 PART 2 PART 3 i1-Q4_K_S 123.8 optimal size/speed/quality
PART 1 PART 2 PART 3 i1-Q4_K_M 130.2 fast, recommended
PART 1 PART 2 PART 3 PART 4 i1-Q5_K_S 150.1
PART 1 PART 2 PART 3 PART 4 i1-Q5_K_M 153.9
PART 1 PART 2 PART 3 PART 4 i1-Q6_K 179.0 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 private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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