← back to catalog · registered 2026-09-12 06:55

mradermacher/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-i1-GGUF

mradermacher 27B GGUF second-order
Abliteration classifier · v1.0.0
M8
Primary method

Repackaging (quantization)

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 3 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • author=mradermacher (M8 quantization producer, never originator)
  • is_gguf=1
  • base_model='DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored' (base has 'abliterated' marker, assume M1 default)
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Downloads · 30-day
0
Likes
2
Model age
today
created 2026-09-12

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.

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Variants by this author 2 formats · 326 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
IQ3 Q2_K Q4_K
Tags
transformers gguf heretic qwen3_8 qwen3_6 uncensored finetune Cold Fusion GAIN Training Multi-stage tuning all use cases unsloth

Related

Total size
36.8 GB
Files
6
Quantizations
4
Registered
2026-09-12 06:55
Last updated on HF
2026-09-12 11:00

Files by quantization

Q4_K 1 file 14.7 GB
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored.i1-Q4_K_S.gguf 14.7 GB 89621ff2 download
IQ3 1 file 11.9 GB
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored.i1-IQ3_M.gguf 11.9 GB 898dc815 download
Q2_K 1 file 10.1 GB
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored.i1-Q2_K.gguf 10.1 GB 21d59502 download
Auxiliary files 3 files 13.0 MB
Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored.imatrix.gguf 13.0 MB 6ca8127d download
README.md 3.67 KB 211d9dfa download
.gitattributes 1.90 KB fd517f58 download

README current version from Hugging Face


base_model: DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
datasets:

  • DavidAU/Polar-STRICT-Datasets
  • DavidAU/F451-STRICT-Datasets
  • DavidAU/THE-DECKARD-Datasets
    language:
  • en
    library_name: transformers
    license: apache-2.0
    mradermacher:
    readme_rev: 1
    quantized_by: mradermacher
    tags:
  • heretic
  • qwen3_8
  • qwen3_6
  • uncensored
  • finetune
  • Cold Fusion
  • GAIN Training
  • Multi-stage tuning
  • all use cases
  • unsloth

About

weighted/imatrix quants of https://huggingface.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored

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

static quants are available at https://huggingface.co/mradermacher/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-GGUF

This is a vision model - mmproj files (if any) will be in the static repository.

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 imatrix 0.1 imatrix file (for creating your own quants)
GGUF i1-Q2_K 11.0 IQ3_XXS probably better
GGUF i1-IQ3_M 12.9
GGUF i1-Q4_K_S 15.9 optimal size/speed/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. Additional thanks to @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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