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---
base_model: dnhkng/RYS-XLarge
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/dnhkng/RYS-XLarge
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#RYS-XLarge-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/RYS-XLarge-i1-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/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q2_K.gguf) | Q2_K | 31.9 | |
| [GGUF](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q3_K_S.gguf) | Q3_K_S | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q3_K_M.gguf) | Q3_K_M | 40.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q3_K_L.gguf) | Q3_K_L | 42.4 | |
| [GGUF](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.IQ4_XS.gguf) | IQ4_XS | 43.1 | |
| [GGUF](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q4_K_S.gguf) | Q4_K_S | 47.0 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q4_K_M.gguf.part2of2) | Q4_K_M | 50.8 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q5_K_S.gguf.part2of2) | Q5_K_S | 55.2 | |
| [PART 1](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q5_K_M.gguf.part2of2) | Q5_K_M | 58.4 | |
| [PART 1](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q6_K.gguf.part2of2) | Q6_K | 69.1 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/RYS-XLarge-GGUF/resolve/main/RYS-XLarge.Q8_0.gguf.part2of2) | Q8_0 | 83.0 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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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