base_model: Doctor-Shotgun/Norobara-ZLoss-8x7B
datasets:
- LDJnr/Capybara
- unalignment/toxic-dpo-v0.1
- LDJnr/Verified-Camel
- HuggingFaceH4/no_robots
- Doctor-Shotgun/no-robots-sharegpt
- Doctor-Shotgun/capybara-sharegpt
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mixtral
About
static quants of https://huggingface.co/Doctor-Shotgun/Norobara-ZLoss-8x7B
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Norobara-ZLoss-8x7B-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
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.