mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-i1-GGUF

About

weighted/imatrix quants of https://huggingface.co/Aratako/Mixtral-8x7B-Instruct-v0.1-upscaled

static quants are available at https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-upscaled-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	16.8	for the desperate
GGUF	i1-IQ1_M	18.7	mostly desperate
GGUF	i1-IQ2_XXS	22.0
GGUF	i1-IQ2_S	24.3
GGUF	i1-IQ2_XS	24.4
GGUF	i1-IQ2_M	27.1
GGUF	i1-Q2_K	30.3	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	31.9	lower quality
GGUF	i1-IQ3_XS	33.7
GGUF	i1-IQ3_S	35.7	beats Q3_K*
GGUF	i1-Q3_K_S	35.7	IQ3_XS probably better
GGUF	i1-IQ3_M	37.5
GGUF	i1-Q3_K_M	39.4	IQ3_S probably better
GGUF	i1-Q3_K_L	42.3	IQ3_M probably better
GGUF	i1-IQ4_XS	43.9
GGUF	i1-Q4_0	46.4	fast, low quality
GGUF	i1-Q4_K_S	46.8	optimal size/speed/quality
PART 1 PART 2	i1-Q4_K_M	49.7	fast, recommended
PART 1 PART 2	i1-Q5_K_S	56.4
PART 1 PART 2	i1-Q5_K_M	58.1
PART 1 PART 2	i1-Q6_K	67.1	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.