base_model:
- mistralai/Mixtral-8x7B-v0.1
- mistralai/Mixtral-8x7B-Instruct-v0.1
- jondurbin/bagel-dpo-8x7b-v0.2
- cognitivecomputations/dolphin-2.7-mixtral-8x7b
- NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss
- ycros/BagelMIsteryTour-v2-8x7B
- smelborp/MixtralOrochi8x7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
About
static quants of https://huggingface.co/rhplus0831/maid-yuzu-v8-alter
weighted/imatrix quants are available at https://huggingface.co/mradermacher/maid-yuzu-v8-alter-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 |
|---|---|---|---|
| GGUF | Q2_K | 17.6 | |
| GGUF | IQ3_XS | 19.5 | |
| GGUF | IQ3_S | 20.7 | beats Q3_K* |
| GGUF | Q3_K_S | 20.7 | |
| GGUF | IQ3_M | 21.7 | |
| GGUF | Q3_K_M | 22.8 | lower quality |
| GGUF | Q3_K_L | 24.4 | |
| GGUF | IQ4_XS | 25.6 | |
| GGUF | Q4_K_S | 27.0 | fast, recommended |
| GGUF | Q4_K_M | 28.7 | fast, recommended |
| GGUF | Q5_K_S | 32.5 | |
| GGUF | Q5_K_M | 33.5 | |
| GGUF | Q6_K | 38.6 | very good quality |
| PART 1 PART 2 | Q8_0 | 49.8 | 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
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.