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mradermacher/Arete-27B-Unlocked-Uncensored-Heretic-GGUF

mradermacher 27B GGUF second-order
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Response includes
  • classification m3
  • files 9
  • author_summary 3529 models
  • readme_text full
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Abliteration classifier · v1.0.0
M3
Primary method

Layer-wise ablation

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 2 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • 'heretic' in model name (Heretic-produced)
  • Heretic uses layer-wise optimization (M3) with underlying direction removal (M1)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
HIGH
Why we say so
name contains 'heretic'; Heretic default extraction is difference-of-means (Arditi 2024)
Downloads · 30-day
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Likes
0
Model age
today
created 2026-11-10

Training datasets

Corpora the author lists in the model card. Datasets tracked in our /datasets catalog carry a category badge linking to the workflow stage. Others open on Hugging Face.

Genealogy 0 direct forks

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Variants by this author 2 formats · 0 downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
apache-2.0
Languages
en
Quantizations
Q2_K Q3_K Q4_K Q6_K Q8_0
Tags
transformers gguf nous-research arete supervised-fine-tuning value-alignment heretic uncensored decensored abliterated reproducible en

Related

Total size
84.1 GB
Files
9
Quantizations
7
Registered
2026-11-10 17:03
Last updated on HF
2026-11-10 17:38

Files by quantization

Q8_0 2 files 27.2 GB
Arete-27B-Unlocked-Uncensored-Heretic.Q8_0.gguf 26.6 GB 703001b6 download
Arete-27B-Unlocked-Uncensored-Heretic.mmproj-Q8_0.gguf 600 MB 1f22ee10 download
Q6_K 1 file 20.6 GB
Arete-27B-Unlocked-Uncensored-Heretic.Q6_K.gguf 20.6 GB 89bf36c8 download
Q4_K 1 file 14.5 GB
Arete-27B-Unlocked-Uncensored-Heretic.Q4_K_S.gguf 14.5 GB 72c96ac2 download
Q3_K 1 file 12.4 GB
Arete-27B-Unlocked-Uncensored-Heretic.Q3_K_M.gguf 12.4 GB 414d280a download
Q2_K 1 file 9.98 GB
Arete-27B-Unlocked-Uncensored-Heretic.Q2_K.gguf 9.98 GB 4f302049 download
F16 1 file 885 MB
Arete-27B-Unlocked-Uncensored-Heretic.mmproj-f16.gguf 885 MB c52fd27a download
Auxiliary files 2 files 5.39 KB
README.md 3.31 KB f4f42b84 download
.gitattributes 2.07 KB ff4c858f download

README current version from Hugging Face


base_model: SolsticeAI/Arete-27B-Unlocked-Uncensored-Heretic
datasets:

  • NousResearch/arete-data-pairs
    language:
  • en
    library_name: transformers
    license: apache-2.0
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • nous-research
  • arete
  • supervised-fine-tuning
  • value-alignment
  • heretic
  • uncensored
  • decensored
  • abliterated
  • reproducible

About

static quants of https://huggingface.co/SolsticeAI/Arete-27B-Unlocked-Uncensored-Heretic

For a convenient overview and download list, visit our model page for this model.

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
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 mmproj-Q8_0 0.7 multi-modal supplement
GGUF mmproj-f16 1.0 multi-modal supplement
GGUF Q2_K 10.8
GGUF Q4_K_S 15.7 fast, recommended
GGUF Q6_K 22.2 very good quality
GGUF Q8_0 28.7 fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

image.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, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

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