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mradermacher/Q2.5-ColdBrew-R1-Forge_V2-i1-GGUF

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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About

weighted/imatrix quants of https://huggingface.co/SvalTek/Q2.5-ColdBrew-R1-ForgeV2

*For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Q2.5-ColdBrew-R1-Forge_V2-i1-GGUF).*

static quants are available at https://huggingface.co/mradermacher/Q2.5-ColdBrew-R1-Forge_V2-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-IQ1_S	2.4	for the desperate
GGUF	i1-IQ1_M	2.6	mostly desperate
GGUF	i1-IQ2_XXS	2.9
GGUF	i1-IQ2_XS	3.1
GGUF	i1-IQ2_S	3.3
GGUF	i1-IQ2_M	3.5
GGUF	i1-Q2KS	3.6	very low quality
GGUF	i1-Q2_K	3.8	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	3.9	lower quality
GGUF	i1-IQ3_XS	4.2
GGUF	i1-Q3KS	4.4	IQ3_XS probably better
GGUF	i1-IQ3_S	4.4	beats Q3_K*
GGUF	i1-IQ3_M	4.5
GGUF	i1-Q3KM	4.8	IQ3_S probably better
GGUF	i1-Q3KL	5.2	IQ3_M probably better
GGUF	i1-IQ4_XS	5.3
GGUF	i1-IQ4_NL	5.6	prefer IQ4_XS
GGUF	i1-Q4_0	5.6	fast, low quality
GGUF	i1-Q4KS	5.6	optimal size/speed/quality
GGUF	i1-Q4KM	5.9	fast, recommended
GGUF	i1-Q4_1	6.1
GGUF	i1-Q5KS	6.7
GGUF	i1-Q5KM	6.9
GGUF	i1-Q6_K	7.9	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.