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mradermacher/Rhea-72b-v0.5-i1-GGUF

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face

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About

weighted/imatrix quants of https://huggingface.co/davidkim205/Rhea-72b-v0.5

the imatrix was calculated on a reduced 40k token set (the "quarter" set) as the full token set caused overflows in the model (likely a model bug)

static quants are available at https://huggingface.co/mradermacher/Rhea-72b-v0.5-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	20.5	for the desperate
GGUF	i1-IQ1_M	21.8	mostly desperate
GGUF	i1-IQ2_XXS	24.0
GGUF	i1-IQ2_XS	26.0
GGUF	i1-IQ2_S	27.6
GGUF	i1-IQ2_M	29.4
GGUF	i1-Q2_K	31.1	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	31.9	lower quality
GGUF	i1-IQ3_XS	34.0
GGUF	i1-IQ3_S	35.6	beats Q3_K*
GGUF	i1-Q3KS	35.6	IQ3_XS probably better
GGUF	i1-IQ3_M	37.3
GGUF	i1-Q3KM	39.3	IQ3_S probably better
GGUF	i1-Q3KL	42.6	IQ3_M probably better
GGUF	i1-IQ4_XS	42.8
GGUF	i1-IQ4_NL	45.1	prefer IQ4_XS
GGUF	i1-Q4_0	45.2	fast, low quality
GGUF	i1-Q4KS	45.3	optimal size/speed/quality
GGUF	i1-Q4KM	47.8	fast, recommended
PART 1 PART 2	i1-Q5KS	53.9
PART 1 PART 2	i1-Q5KM	55.4
PART 1 PART 2	i1-Q6_K	63.4	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.