---
base_model:
- mlabonne/AlphaMonarch-7B
- FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- OmnicromsBrain/NeuralStar-7b-Lazy
exported_from: OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- OmnicromsBrain/NeuralStar-7b-Lazy
---
## About

<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type:  -->
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static quants of https://huggingface.co/OmnicromsBrain/NeuralStar_AlphaWriter_4x7b

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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/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q2_K.gguf) | Q2_K | 8.9 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.IQ3_XS.gguf) | IQ3_XS | 10.0 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q3_K_S.gguf) | Q3_K_S | 10.5 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.IQ3_S.gguf) | IQ3_S | 10.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.IQ3_M.gguf) | IQ3_M | 10.7 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q3_K_M.gguf) | Q3_K_M | 11.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q3_K_L.gguf) | Q3_K_L | 12.6 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.IQ4_XS.gguf) | IQ4_XS | 13.1 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q4_K_S.gguf) | Q4_K_S | 13.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q4_K_M.gguf) | Q4_K_M | 14.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q5_K_S.gguf) | Q5_K_S | 16.7 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q5_K_M.gguf) | Q5_K_M | 17.2 |  |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q6_K.gguf) | Q6_K | 19.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF/resolve/main/NeuralStar_AlphaWriter_4x7b.Q8_0.gguf) | Q8_0 | 25.8 | 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

## 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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