---
datasets:
- oscar-corpus/OSCAR-2301
- wikipedia
- bjoernp/tagesschau-2018-2023
exported_from: TitanML/LeoLM-hessianai-13b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About

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static quants of https://huggingface.co/TitanML/LeoLM-hessianai-13b


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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/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q2_K.gguf) | Q2_K | 5.0 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.IQ3_XS.gguf) | IQ3_XS | 5.5 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q3_K_S.gguf) | Q3_K_S | 5.8 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.IQ3_M.gguf) | IQ3_M | 6.1 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q3_K_L.gguf) | Q3_K_L | 7.0 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q5_K_S.gguf) | Q5_K_S | 9.1 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q5_K_M.gguf) | Q5_K_M | 9.3 |  |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LeoLM-hessianai-13b-GGUF/resolve/main/LeoLM-hessianai-13b.Q8_0.gguf) | Q8_0 | 13.9 | 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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