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-17-ORPO	mlabonne/orpo-dpo-mix-40k	multilingual	transformers	gemma	https://ai.google.dev/gemma/terms	readme_rev 1	mradermacher	nlp code

About

static quants of https://huggingface.co/ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-2-2b-jpn-it-abliterated-17-ORPO-i1-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	Q2_K	1.3
GGUF	Q3_K_S	1.5
GGUF	Q3_K_M	1.6	lower quality
GGUF	Q3_K_L	1.7
GGUF	IQ4_XS	1.7
GGUF	Q4_K_S	1.7	fast, recommended
GGUF	Q4_K_M	1.8	fast, recommended
GGUF	Q5_K_S	2.0
GGUF	Q5_K_M	2.0
GGUF	Q6_K	2.3	very good quality
GGUF	Q8_0	2.9	fast, best quality
GGUF	f16	5.3	16 bpw, overkill

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.