guanaco-33b-lxctx-PI-16384-LoRA-fp16-GGUF – AI Model by mradermacher | AlphaNeural AI

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guanaco-33b-lxctx-PI-16384-LoRA-fp16-GGUF

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About

static quants of https://huggingface.co/Panchovix/guanaco-33b-lxctx-PI-16384-LoRA-fp16

weighted/imatrix quants are available at https://huggingface.co/mradermacher/guanaco-33b-lxctx-PI-16384-LoRA-fp16-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	12.1
GGUF	IQ3_XS	13.4
GGUF	IQ3_S	14.2	beats Q3_K*
GGUF	Q3_K_S	14.2
GGUF	IQ3_M	15.0
GGUF	Q3_K_M	15.9	lower quality
GGUF	Q3_K_L	17.4
GGUF	IQ4_XS	17.6
GGUF	Q4_K_S	18.6	fast, recommended
GGUF	Q4_K_M	19.7	fast, recommended
GGUF	Q5_K_S	22.5
GGUF	Q5_K_M	23.1
GGUF	Q6_K	26.8	very good quality
GGUF	Q8_0	34.7	fast, best quality

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.