---
base_model:
- zhengr/MixTAO-7Bx2-MoE-v8.1
- zhengr/MixTAO-7Bx2-MoE-v8.1
- zhengr/MixTAO-7Bx2-MoE-v8.1
- zhengr/MixTAO-7Bx2-MoE-v8.1
- zhengr/MixTAO-7Bx2-MoE-v8.1
exported_from: allknowingroger/TaoPassthrough-15B-s
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- zhengr/MixTAO-7Bx2-MoE-v8.1
---
## About

static quants of https://huggingface.co/allknowingroger/TaoPassthrough-15B-s


<!-- provided-files -->
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/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q2_K.gguf) | Q2_K | 7.3 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.IQ3_XS.gguf) | IQ3_XS | 8.1 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q3_K_S.gguf) | Q3_K_S | 8.6 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.IQ3_S.gguf) | IQ3_S | 8.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.IQ3_M.gguf) | IQ3_M | 8.8 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q3_K_M.gguf) | Q3_K_M | 9.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q3_K_L.gguf) | Q3_K_L | 10.3 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.IQ4_XS.gguf) | IQ4_XS | 10.6 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q4_0.gguf) | Q4_0 | 11.1 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q4_K_S.gguf) | Q4_K_S | 11.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q4_K_M.gguf) | Q4_K_M | 11.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q5_K_S.gguf) | Q5_K_S | 13.5 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q5_K_M.gguf) | Q5_K_M | 13.9 |  |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q6_K.gguf) | Q6_K | 16.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TaoPassthrough-15B-s-GGUF/resolve/main/TaoPassthrough-15B-s.Q8_0.gguf) | Q8_0 | 20.6 | 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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