gemma-ko-7b-instruct-v0.52-GGUF/README.md

base_model, language, library_name, license, license_link, license_name, quantized_by, tags

base_model	language	library_name	license	license_link	license_name	quantized_by	tags
lemon-mint/gemma-ko-7b-instruct-v0.52	ko en	transformers	other	https://ai.google.dev/gemma/terms	gemma-terms-of-use	mradermacher	korean gemma pytorch

About

static quants of https://huggingface.co/lemon-mint/gemma-ko-7b-instruct-v0.52

weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-ko-7b-instruct-v0.52-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	3.6
GGUF	Q3_K_S	4.1
GGUF	Q3_K_M	4.5	lower quality
GGUF	Q3_K_L	4.8
GGUF	IQ4_XS	4.9
GGUF	Q4_K_S	5.1	fast, recommended
GGUF	Q4_K_M	5.4	fast, recommended
GGUF	Q5_K_S	6.1
GGUF	Q5_K_M	6.2
GGUF	Q6_K	7.1	very good quality
GGUF	Q8_0	9.2	fast, best quality
GGUF	f16	17.2	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.