athena-120b-i1-GGUF / README.md
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metadata
exported_from: ibivibiv/athena-120b
language:
  - en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
  - merge

About

weighted/imatrix quants of https://huggingface.co/ibivibiv/athena-120b

static quants are available at https://huggingface.co/mradermacher/athena-120b-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_M 41.4
GGUF i1-Q2_K 45.1 IQ3_XXS probably better
GGUF i1-IQ3_XXS 47.1 lower quality
PART 1 PART 2 i1-IQ3_XS 50.1
PART 1 PART 2 i1-Q3_K_S 52.7 IQ3_XS probably better
PART 1 PART 2 i1-Q3_K_M 58.8 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 63.9 IQ3_M probably better
PART 1 PART 2 i1-IQ4_XS 65.1
PART 1 PART 2 i1-Q4_0 68.9 fast, low quality
PART 1 PART 2 i1-Q4_K_S 69.2 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 73.1 fast, recommended
PART 1 PART 2 i1-Q5_K_S 83.7
PART 1 PART 2 i1-Q5_K_M 86.0
PART 1 PART 2 PART 3 i1-Q6_K 99.6 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.