saily_100b-i1-GGUF / README.md
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metadata
exported_from: deepnight-research/saily_100b
language:
  - en
library_name: transformers
license: mit
license_link: LICENSE
license_name: deepnight-responsible-ai
quantized_by: mradermacher

About

weighted/imatrix quants of https://huggingface.co/deepnight-research/saily_100b

static quants are available at https://huggingface.co/mradermacher/saily_100b-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-IQ2_XXS 31.2
GGUF i1-IQ2_XS 34.7
GGUF i1-IQ2_M 39.7
GGUF i1-Q2_K 43.3 IQ3_XXS probably better
GGUF i1-IQ3_XXS 45.3 lower quality
GGUF i1-IQ3_XS 48.2
PART 1 PART 2 i1-Q3_K_S 50.8 IQ3_XS probably better
PART 1 PART 2 i1-IQ3_S 51.0 beats Q3_K*
PART 1 PART 2 i1-IQ3_M 52.7
PART 1 PART 2 i1-Q3_K_M 56.7 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 61.8 IQ3_M probably better
PART 1 PART 2 i1-IQ4_XS 62.9
PART 1 PART 2 i1-Q4_0 66.7 fast, low quality
PART 1 PART 2 i1-Q4_K_S 66.9 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 70.7 fast, recommended
PART 1 PART 2 i1-Q5_K_S 81.1
PART 1 PART 2 i1-Q5_K_M 83.3
PART 1 PART 2 i1-Q6_K 96.7 practically like static Q6_K

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

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.