Transformers
GGUF
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reasoning
preference_learning
nca
Inference Endpoints
imatrix
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---
base_model: openbmb/Eurux-8x22b-kto
datasets:
- openbmb/UltraInteract_sft
- openbmb/UltraInteract_pair
- openbmb/UltraFeedback
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- reasoning
- preference_learning
- nca
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
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<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/openbmb/Eurux-8x22b-kto
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Eurux-8x22b-kto-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/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 | |
| [GGUF](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q2_K_S.gguf) | i1-Q2_K_S | 48.2 | very low quality |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 | |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Eurux-8x22b-kto-i1-GGUF/resolve/main/Eurux-8x22b-kto.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | practically like static Q6_K |
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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