mxbai-rerank-large-v2-GGUF/README.md

base_model, language, library_name, license, quantized_by

base_model	language	library_name	license	quantized_by
mixedbread-ai/mxbai-rerank-large-v2	af am ar as az be bg bn br bs ca cs cy da de el en eo es et eu fa ff fi fr fy ga gd gl gn gu ha he hi hr ht hu hy id ig is it ja jv ka kk km kn ko ku ky la lg li ln lo lt lv mg mk ml mn mr ms my ne nl no ns om or pa pl ps pt qu rm ro ru sa sc sd si sk sl so sq sr ss su sv sw ta te th tl tn tr ug uk ur uz vi wo xh yi yo zh zu	transformers	apache-2.0	mradermacher

About

static quants of https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2

weighted/imatrix quants are available at https://huggingface.co/mradermacher/mxbai-rerank-large-v2-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	0.8
GGUF	Q3_K_S	0.9
GGUF	Q3_K_M	0.9	lower quality
GGUF	Q3_K_L	1.0
GGUF	IQ4_XS	1.0
GGUF	Q4_K_S	1.0	fast, recommended
GGUF	Q4_K_M	1.1	fast, recommended
GGUF	Q5_K_S	1.2
GGUF	Q5_K_M	1.2
GGUF	Q6_K	1.4	very good quality
GGUF	Q8_0	1.7	fast, best quality
GGUF	f16	3.2	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.