Transformers
Mixture of Experts
mixtral
sharegpt
axolotl
Inference Endpoints
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metadata
base_model: MaziyarPanahi/Goku-8x22B-v0.2
datasets:
  - MaziyarPanahi/WizardLM_evol_instruct_V2_196k
  - microsoft/orca-math-word-problems-200k
  - teknium/OpenHermes-2.5
language:
  - fr
  - it
  - de
  - es
  - en
library_name: transformers
license: apache-2.0
model_creator: MaziyarPanahi
model_name: Goku-8x22B-v0.2
quantized_by: mradermacher
tags:
  - moe
  - mixtral
  - sharegpt
  - axolotl

About

static quants of https://huggingface.co/MaziyarPanahi/Goku-8x22B-v0.2

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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
PART 1 PART 2 Q2_K 52.2
PART 1 PART 2 IQ3_S 61.6 beats Q3_K*
PART 1 PART 2 Q3_K_S 61.6
PART 1 PART 2 IQ3_M 64.6
PART 1 PART 2 Q3_K_M 67.9 lower quality
PART 1 PART 2 Q3_K_L 72.7
PART 1 PART 2 Q4_K_S 80.6 fast, recommended
PART 1 PART 2 Q4_K_M 85.7 fast, recommended
PART 1 PART 2 Q5_K_S 97.1
PART 1 PART 2 PART 3 Q6_K 115.6 very good quality
PART 1 PART 2 PART 3 PART 4 Q8_0 149.5 fast, best quality

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.