base_model, datasets, language, library_name, license, license_link, mradermacher, quantized_by, tags

base_model	datasets	language	library_name	license	license_link	mradermacher	quantized_by	tags
ymcki/gemma-2-2b-jpn-it-abliterated-18	mlabonne/harmless_alpaca mlabonne/harmful_behaviors	multilingual	transformers	gemma	https://ai.google.dev/gemma/terms	readme_rev 1	mradermacher	nlp code

About

weighted/imatrix quants of https://huggingface.co/ymcki/gemma-2-2b-jpn-it-abliterated-18

For a convenient overview and download list, visit our model page for this model.

static quants are available at https://huggingface.co/mradermacher/gemma-2-2b-jpn-it-abliterated-18-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	0.9	for the desperate
GGUF	i1-IQ1_M	1.0	mostly desperate
GGUF	i1-IQ2_XXS	1.0
GGUF	i1-IQ2_XS	1.1
GGUF	i1-IQ2_S	1.1
GGUF	i1-IQ2_M	1.2
GGUF	i1-Q2_K_S	1.3	very low quality
GGUF	i1-IQ3_XXS	1.3	lower quality
GGUF	i1-Q2_K	1.3	IQ3_XXS probably better
GGUF	i1-IQ3_XS	1.4
GGUF	i1-IQ3_S	1.5	beats Q3_K*
GGUF	i1-Q3_K_S	1.5	IQ3_XS probably better
GGUF	i1-IQ3_M	1.5
GGUF	i1-Q3_K_M	1.6	IQ3_S probably better
GGUF	i1-Q3_K_L	1.7	IQ3_M probably better
GGUF	i1-IQ4_XS	1.7
GGUF	i1-IQ4_NL	1.7	prefer IQ4_XS
GGUF	i1-Q4_0	1.7	fast, low quality
GGUF	i1-Q4_K_S	1.7	optimal size/speed/quality
GGUF	i1-Q4_K_M	1.8	fast, recommended
GGUF	i1-Q4_1	1.9
GGUF	i1-Q5_K_S	2.0
GGUF	i1-Q5_K_M	2.0
GGUF	i1-Q6_K	2.3	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.