mradermacher/Ahma-7B-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/Finnish-NLP/Ahma-7B

static quants are available at https://huggingface.co/mradermacher/Ahma-7B-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	1.8	for the desperate
GGUF	i1-IQ1_M	1.9	mostly desperate
GGUF	i1-IQ2_XXS	2.1
GGUF	i1-IQ2_XS	2.3
GGUF	i1-IQ2_S	2.4
GGUF	i1-IQ2_M	2.6
GGUF	i1-Q2_K	2.8	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	2.8	lower quality
GGUF	i1-IQ3_XS	3.1
GGUF	i1-IQ3_S	3.2	beats Q3_K*
GGUF	i1-Q3_K_S	3.2	IQ3_XS probably better
GGUF	i1-IQ3_M	3.4
GGUF	i1-Q3_K_M	3.6	IQ3_S probably better
GGUF	i1-Q3_K_L	3.9	IQ3_M probably better
GGUF	i1-IQ4_XS	3.9
GGUF	i1-Q4_0_4_4	4.1	fast on arm, low quality
GGUF	i1-Q4_0_4_8	4.1	fast on arm+i8mm, low quality
GGUF	i1-Q4_0_8_8	4.1	fast on arm+sve, low quality
GGUF	i1-Q4_0	4.1	fast, low quality
GGUF	i1-Q4_K_S	4.1	optimal size/speed/quality
GGUF	i1-Q4_K_M	4.4	fast, recommended
GGUF	i1-Q5_K_S	5.0
GGUF	i1-Q5_K_M	5.1
GGUF	i1-Q6_K	5.8	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.