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About

weighted/imatrix quants of https://huggingface.co/nuprl/MultiPL-T-CodeLlama_34b

static quants are available at https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-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 7.4 for the desperate
GGUF i1-IQ1_M 8.0 mostly desperate
GGUF i1-IQ2_XXS 9.1
GGUF i1-IQ2_XS 10.1
GGUF i1-IQ2_S 10.7
GGUF i1-IQ2_M 11.6
GGUF i1-Q2_K 12.6 IQ3_XXS probably better
GGUF i1-IQ3_XXS 13.1 lower quality
GGUF i1-IQ3_XS 14.0
GGUF i1-Q3_K_S 14.7 IQ3_XS probably better
GGUF i1-IQ3_S 14.8 beats Q3_K*
GGUF i1-IQ3_M 15.3
GGUF i1-Q3_K_M 16.4 IQ3_S probably better
GGUF i1-Q3_K_L 17.9 IQ3_M probably better
GGUF i1-IQ4_XS 18.2
GGUF i1-Q4_0 19.2 fast, low quality
GGUF i1-Q4_K_S 19.3 optimal size/speed/quality
GGUF i1-Q4_K_M 20.3 fast, recommended
GGUF i1-Q5_K_S 23.3
GGUF i1-Q5_K_M 23.9
GGUF i1-Q6_K 27.8 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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