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

---

base_model: Na0s/Mixtral-8x7B-Instruct-v0.1-exhaustive-LoRA datasets:

• Na0s/sft-ready-Text-Generation-Augmented-Data

language:

• en

library_name: transformers quantized_by: mradermacher

---

About

weighted/imatrix quants of https://huggingface.co/Na0s/Mixtral-8x7B-Instruct-v0.1-exhaustive-LoRA

static quants are available at https://huggingface.co/mradermacher/Mixtral-8x7B-Instruct-v0.1-exhaustive-LoRA-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	9.9	for the desperate
GGUF	i1-IQ1_M	10.9	mostly desperate
GGUF	i1-IQ2_XXS	12.7
GGUF	i1-IQ2_XS	14.0
GGUF	i1-IQ2_S	14.2
GGUF	i1-IQ2_M	15.6
GGUF	i1-Q2K	17.4	IQ3XXS probably better
GGUF	i1-IQ3_XXS	18.3	lower quality
GGUF	i1-IQ3_XS	19.5
GGUF	i1-IQ3S	20.5	beats Q3K*
GGUF	i1-Q3KS	20.5	IQ3XS probably better
GGUF	i1-IQ3_M	21.5
GGUF	i1-Q3KM	22.6	IQ3S probably better
GGUF	i1-Q3KL	24.3	IQ3M probably better
GGUF	i1-IQ4_XS	25.2
GGUF	i1-Q4_0	26.7	fast, low quality
GGUF	i1-Q4K_S	26.8	optimal size/speed/quality
GGUF	i1-Q4K_M	28.5	fast, recommended
GGUF	i1-Q5K_S	32.3
GGUF	i1-Q5K_M	33.3
GGUF	i1-Q6K	38.5	practically like static Q6K

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. 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.