30B-Lazarus-i1-GGUF / README.md
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metadata
exported_from: CalderaAI/30B-Lazarus
language:
  - en
library_name: transformers
quantized_by: mradermacher
tags:
  - llama
  - alpaca
  - cot
  - vicuna
  - uncensored
  - merge
  - mix

About

weighted/imatrix quants of https://huggingface.co/CalderaAI/30B-Lazarus

static quants are available at https://huggingface.co/mradermacher/30B-Lazarus-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 8.8
GGUF i1-IQ2_XS 9.7
GGUF i1-IQ2_S 10.5
GGUF i1-IQ2_M 11.3
GGUF i1-Q2_K 12.1 IQ3_XXS probably better
GGUF i1-IQ3_XXS 12.4 lower quality
GGUF i1-IQ3_XS 13.4
GGUF i1-IQ3_S 14.2 beats Q3_K*
GGUF i1-Q3_K_S 14.2 IQ3_XS probably better
GGUF i1-IQ3_M 15.0
GGUF i1-Q3_K_M 15.9 IQ3_S probably better
GGUF i1-Q3_K_L 17.4 IQ3_M probably better
GGUF i1-IQ4_XS 17.4
GGUF i1-Q4_0 18.5 fast, low quality
GGUF i1-Q4_K_S 18.6 optimal size/speed/quality
GGUF i1-Q5_K_S 22.5
GGUF i1-Q5_K_M 23.1
GGUF i1-Q6_K 26.8 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.