---
base_model:
- Yuma42/KangalKhan-Ruby-7B-Fixed
- Yuma42/KangalKhan-Beta-Sapphire-7B
exported_from: Yuma42/KangalKhan-Alpha-Emerald-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- Yuma42/KangalKhan-Ruby-7B-Fixed
- Yuma42/KangalKhan-Beta-Sapphire-7B
---
## About

static quants of https://huggingface.co/Yuma42/KangalKhan-Alpha-Emerald-7B


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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/KangalKhan-Alpha-Emerald-7B-GGUF/resolve/main/KangalKhan-Alpha-Emerald-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |


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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