---
base_model: Elfrino/ContextualKunoichi_KTO-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About

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weighted/imatrix quants of https://huggingface.co/Elfrino/ContextualKunoichi_KTO-7B

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static quants are available at https://huggingface.co/mradermacher/ContextualKunoichi_KTO-7B-GGUF
## 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/ContextualKunoichi_KTO-7B-i1-GGUF/resolve/main/ContextualKunoichi_KTO-7B.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/ContextualKunoichi_KTO-7B-i1-GGUF/resolve/main/ContextualKunoichi_KTO-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ContextualKunoichi_KTO-7B-i1-GGUF/resolve/main/ContextualKunoichi_KTO-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 |  |
| [GGUF](https://huggingface.co/mradermacher/ContextualKunoichi_KTO-7B-i1-GGUF/resolve/main/ContextualKunoichi_KTO-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.

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