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About

static quants of https://huggingface.co/sam-paech/Quill-v1

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Quill-v1-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 3.9
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_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):

image.png

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.

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gemma2

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Model tree for mradermacher/Quill-v1-GGUF

Base model

google/gemma-2-9b
Finetuned
sam-paech/Quill-v1
Quantized
(2)
this model

Dataset used to train mradermacher/Quill-v1-GGUF