README.md

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base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
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language:
- en
- fr
- it
- pt
- hi
- es
- th
- de
library_name: transformers
license: llama3.3
quantized_by: mradermacher
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- abliterated
- uncensored
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
static quants of https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned

<!-- 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/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q2_K.gguf) | Q2_K | 26.5 |  |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q3_K_S.gguf) | Q3_K_S | 31.0 |  |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q3_K_L.gguf) | Q3_K_L | 37.2 |  |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q5_K_S.gguf) | Q5_K_S | 48.8 |  |
| [PART 1](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3.3-70B-Instruct-abliterated-finetuned-GGUF/resolve/main/Llama-3.3-70B-Instruct-abliterated-finetuned.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | 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. 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.

<!-- end -->