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 are available at https://huggingface.co/mradermacher/Goku-8x22B-v0.2-i1-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 |
|---|---|---|---|
| PART 1 PART 2 | Q2_K | 52.2 | |
| PART 1 PART 2 | IQ3_XS | 58.3 | |
| 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 | IQ4_XS | 76.5 | |
| 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 | Q5_K_M | 100.1 | |
| 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):
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.