base_model: MaziyarPanahi/Goku-8x22B-v0.1
datasets:
- philschmid/guanaco-sharegpt-style
language:
- fr
- it
- de
- es
- en
library_name: transformers
license: apache-2.0
model_creator: MaziyarPanahi
model_name: Goku-8x22B-v0.1
quantized_by: mradermacher
tags:
- moe
- mixtral
- sharegpt
- axolotl
About
weighted/imatrix quants of https://huggingface.co/MaziyarPanahi/Goku-8x22B-v0.1
static quants are available at https://huggingface.co/mradermacher/Goku-8x22B-v0.1-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 | 38.0 | |
| GGUF | i1-IQ2_XS | 42.1 | |
| GGUF | i1-IQ2_M | 46.8 | |
| PART 1 PART 2 | i1-Q2_K | 52.2 | IQ3_XXS probably better |
| PART 1 PART 2 | i1-IQ3_XXS | 55.0 | lower quality |
| PART 1 PART 2 | i1-IQ3_XS | 58.3 | |
| PART 1 PART 2 | i1-IQ3_S | 61.6 | beats Q3_K* |
| PART 1 PART 2 | i1-Q3_K_S | 61.6 | IQ3_XS probably better |
| PART 1 PART 2 | i1-IQ3_M | 64.6 | |
| PART 1 PART 2 | i1-Q3_K_M | 67.9 | IQ3_S probably better |
| PART 1 PART 2 | i1-Q3_K_L | 72.7 | IQ3_M probably better |
| PART 1 PART 2 | i1-IQ4_XS | 75.6 | |
| PART 1 PART 2 | i1-Q4_0 | 80.0 | fast, low quality |
| PART 1 PART 2 | i1-Q4_K_S | 80.6 | optimal size/speed/quality |
| PART 1 PART 2 | i1-Q4_K_M | 85.7 | fast, recommended |
| PART 1 PART 2 | i1-Q5_K_S | 97.1 | |
| PART 1 PART 2 PART 3 | i1-Q5_K_M | 100.1 | |
| PART 1 PART 2 PART 3 | i1-Q6_K | 115.6 | practically like static Q6_K |
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. Additional thanks to @nicoboss for giving me access to his hardware for calculating the imatrix for these quants.