base_model, language, library_name, quantized_by, tags

base_model	language	library_name	quantized_by	tags
Crystalcareai/CrystalMistral-14b	en	transformers	mradermacher	merge mergekit lazymergekit eren23/dpo-binarized-NeuralTrix-7B eren23/dpo-binarized-NeuralTrix-7B

About

static quants of https://huggingface.co/Crystalcareai/CrystalMistral-14b

weighted/imatrix quants are available at https://huggingface.co/mradermacher/CrystalMistral-14b-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	5.4
GGUF	Q3_K_S	6.3
GGUF	Q3_K_M	7.0	lower quality
GGUF	Q3_K_L	7.6
GGUF	IQ4_XS	7.8
GGUF	Q4_0_4_4	8.1	fast on arm, low quality
GGUF	Q4_K_S	8.2	fast, recommended
GGUF	Q4_K_M	8.7	fast, recommended
GGUF	Q5_K_S	9.9
GGUF	Q5_K_M	10.2
GGUF	Q6_K	11.8	very good quality
GGUF	Q8_0	15.2	fast, best quality

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.