mradermacher/Norobara-ZLoss-8x7B-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/Doctor-Shotgun/Norobara-ZLoss-8x7B

static quants are available at https://huggingface.co/mradermacher/Norobara-ZLoss-8x7B-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	10.1	for the desperate
GGUF	i1-IQ2_XXS	12.8
GGUF	i1-IQ2_XS	14.2
GGUF	i1-IQ2_S	14.4
GGUF	i1-IQ2_M	15.8
GGUF	i1-Q2_K	17.6	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	18.5	lower quality
GGUF	i1-IQ3_XS	19.5
GGUF	i1-IQ3_S	20.7	beats Q3_K*
GGUF	i1-Q3_K_S	20.7	IQ3_XS probably better
GGUF	i1-IQ3_M	21.7
GGUF	i1-Q3_K_M	22.8	IQ3_S probably better
GGUF	i1-Q3_K_L	24.4	IQ3_M probably better
GGUF	i1-IQ4_XS	25.3
GGUF	i1-Q4_K_S	27.0	optimal size/speed/quality
GGUF	i1-Q4_K_M	28.7	fast, recommended
GGUF	i1-Q5_K_S	32.5
GGUF	i1-Q5_K_M	33.5
GGUF	i1-Q6_K	38.6	practically like static Q6_K

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

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.