gemma-2-2b-jpn-it-translate-i1-GGUF/README.md

base_model, language, library_name, mradermacher, quantized_by, tags

base_model	language	library_name	mradermacher	quantized_by	tags
webbigdata/gemma-2-2b-jpn-it-translate	ja en	transformers	readme_rev 1	mradermacher	translation

About

weighted/imatrix quants of https://huggingface.co/webbigdata/gemma-2-2b-jpn-it-translate

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-translate-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-IQ2_XS	1.1
GGUF	i1-IQ3_XS	1.4
GGUF	i1-IQ4_XS	1.7
GGUF	i1-Q4_K_M	1.8	fast, recommended
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.