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

base_model	datasets	language	library_name	license	quantized_by	tags
SejongKRX/RevThink-Sejong-Gemma-it-v1	gyung/RevThink-financial-mmlu-ko	en	transformers	apache-2.0	mradermacher	text-generation-inference transformers unsloth gemma2 trl

About

static quants of https://huggingface.co/SejongKRX/RevThink-Sejong-Gemma-it-v1

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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	3.9
GGUF	Q3_K_S	4.4
GGUF	Q3_K_M	4.9	lower quality
GGUF	Q3_K_L	5.2
GGUF	IQ4_XS	5.3
GGUF	Q4_0_4_4	5.5	fast on arm, low quality
GGUF	Q4_K_S	5.6	fast, recommended
GGUF	Q4_K_M	5.9	fast, recommended
GGUF	Q5_K_S	6.6
GGUF	Q5_K_M	6.7
GGUF	Q6_K	7.7	very good quality
GGUF	Q8_0	9.9	fast, best quality
GGUF	f16	18.6	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. 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.