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About

weighted/imatrix quants of https://huggingface.co/nvidia/Nemotron-Mini-4B-Instruct

static quants are available at https://huggingface.co/mradermacher/Nemotron-Mini-4B-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 1.5 for the desperate
GGUF i1-IQ1_M 1.5 mostly desperate
GGUF i1-IQ2_XXS 1.6
GGUF i1-IQ2_XS 1.7
GGUF i1-IQ2_S 1.8
GGUF i1-IQ2_M 1.9
GGUF i1-IQ3_XXS 2.0 lower quality
GGUF i1-Q2_K 2.0 IQ3_XXS probably better
GGUF i1-IQ3_XS 2.2
GGUF i1-IQ3_S 2.2 beats Q3_K*
GGUF i1-Q3_K_S 2.2 IQ3_XS probably better
GGUF i1-IQ3_M 2.3
GGUF i1-Q3_K_M 2.4 IQ3_S probably better
GGUF i1-Q3_K_L 2.6 IQ3_M probably better
GGUF i1-IQ4_XS 2.6
GGUF i1-Q4_0_4_4 2.7 fast on arm, low quality
GGUF i1-Q4_0_4_8 2.7 fast on arm+i8mm, low quality
GGUF i1-Q4_0_8_8 2.7 fast on arm+sve, low quality
GGUF i1-Q4_0 2.7 fast, low quality
GGUF i1-Q4_K_S 2.7 optimal size/speed/quality
GGUF i1-Q4_K_M 2.8 fast, recommended
GGUF i1-Q5_K_S 3.1
GGUF i1-Q5_K_M 3.2
GGUF i1-Q6_K 3.5 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 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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