---
base_model: divinetaco/aranea-ancilla-116b-v1.0
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About

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static quants of https://huggingface.co/divinetaco/aranea-ancilla-116b-v1.0

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-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/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q2_K.gguf) | Q2_K | 43.0 |  |
| [GGUF](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ3_XS.gguf) | IQ3_XS | 47.9 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q3_K_S.gguf.part2of2) | Q3_K_S | 50.4 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ3_S.gguf.part2of2) | IQ3_S | 50.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ3_M.gguf.part2of2) | IQ3_M | 52.3 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q3_K_M.gguf.part2of2) | Q3_K_M | 56.3 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q3_K_L.gguf.part2of2) | Q3_K_L | 61.3 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.IQ4_XS.gguf.part2of2) | IQ4_XS | 63.1 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q4_K_S.gguf.part2of2) | Q4_K_S | 66.4 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q4_K_M.gguf.part2of2) | Q4_K_M | 70.2 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q5_K_S.gguf.part2of2) | Q5_K_S | 80.5 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q5_K_M.gguf.part2of2) | Q5_K_M | 82.7 |  |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q6_K.gguf.part2of2) | Q6_K | 96.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/aranea-ancilla-116b-v1.0-GGUF/resolve/main/aranea-ancilla-116b-v1.0.Q8_0.gguf.part3of3) | Q8_0 | 124.3 | 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

## 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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