---
base_model: argilla/notux-8x7b-v1
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
language:
- en
- de
- es
- fr
- it
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- dpo
- rlaif
- preference
- ultrafeedback
- moe
---
## About

static quants of https://huggingface.co/argilla/notux-8x7b-v1

<!-- provided-files -->
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](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q2_K.gguf) | Q2_K | 17.6 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.IQ3_XS.gguf) | IQ3_XS | 19.5 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.IQ3_S.gguf) | IQ3_S | 20.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q3_K_S.gguf) | Q3_K_S | 20.7 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.IQ3_M.gguf) | IQ3_M | 21.7 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q3_K_M.gguf) | Q3_K_M | 22.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q3_K_L.gguf) | Q3_K_L | 24.4 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.IQ4_XS.gguf) | IQ4_XS | 25.6 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q4_K_S.gguf) | Q4_K_S | 27.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q4_K_M.gguf) | Q4_K_M | 28.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q5_K_S.gguf) | Q5_K_S | 32.5 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q5_K_M.gguf) | Q5_K_M | 33.5 |  |
| [GGUF](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q6_K.gguf) | Q6_K | 38.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/notux-8x7b-v1-GGUF/resolve/main/notux-8x7b-v1.Q8_0.gguf.part2of2) | Q8_0 | 49.8 | fast, best quality |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

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