mradermacher/MN-Chinofun-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/djuna/MN-Chinofun

static quants are available at https://huggingface.co/mradermacher/MN-Chinofun-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	3.1	for the desperate
GGUF	i1-IQ1_M	3.3	mostly desperate
GGUF	i1-IQ2_XXS	3.7
GGUF	i1-IQ2_XS	4.0
GGUF	i1-IQ2_S	4.2
GGUF	i1-IQ2_M	4.5
GGUF	i1-Q2_K	4.9	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	5.0	lower quality
GGUF	i1-IQ3_XS	5.4
GGUF	i1-Q3_K_S	5.6	IQ3_XS probably better
GGUF	i1-IQ3_S	5.7	beats Q3_K*
GGUF	i1-IQ3_M	5.8
GGUF	i1-Q3_K_M	6.2	IQ3_S probably better
GGUF	i1-Q3_K_L	6.7	IQ3_M probably better
GGUF	i1-IQ4_XS	6.8
GGUF	i1-Q4_0_4_4	7.2	fast on arm, low quality
GGUF	i1-Q4_0_4_8	7.2	fast on arm+i8mm, low quality
GGUF	i1-Q4_0_8_8	7.2	fast on arm+sve, low quality
GGUF	i1-Q4_0	7.2	fast, low quality
GGUF	i1-Q4_K_S	7.2	optimal size/speed/quality
GGUF	i1-Q4_K_M	7.6	fast, recommended
GGUF	i1-Q5_K_S	8.6
GGUF	i1-Q5_K_M	8.8
GGUF	i1-Q6_K	10.2	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. 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.