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metadata
exported_from: MarsupialAI/KitchenSink_103b
language:
  - en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
  - rp
  - erp
  - chat
  - storywriting

About

weighted/imatrix quants of https://huggingface.co/MarsupialAI/KitchenSink_103b

static quants are available at https://huggingface.co/mradermacher/KitchenSink_103b-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 27.7
GGUF i1-IQ2_XS 30.8
GGUF i1-IQ2_M 35.1
GGUF i1-Q2_K 38.3 IQ3_XXS probably better
GGUF i1-IQ3_XXS 40.0 lower quality
GGUF i1-IQ3_XS 42.6
GGUF i1-Q3_K_S 44.9 IQ3_XS probably better
GGUF i1-IQ3_S 45.0 beats Q3_K*
GGUF i1-IQ3_M 46.5
PART 1 PART 2 i1-Q3_K_M 50.0 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 54.5 IQ3_M probably better
PART 1 PART 2 i1-IQ4_XS 55.5
PART 1 PART 2 i1-Q4_0 58.8 fast, low quality
PART 1 PART 2 i1-Q4_K_S 59.0 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 62.3 fast, recommended
PART 1 PART 2 i1-Q5_K_S 71.4
PART 1 PART 2 i1-Q5_K_M 73.3
PART 1 PART 2 i1-Q6_K 85.1 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.