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Johnblick187/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored

Johnblick187 27B multimodal second-order
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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
0
Likes
0
Model age
today
created 2026-10-12

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.

Genealogy 0 direct forks

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Metadata

License
apache-2.0
Languages
en
Tags
transformers safetensors qwen3_5 image-text-to-text heretic qwen3_8 qwen3_6 uncensored finetune Cold Fusion GAIN Training Multi-stage tuning

Related

Total size
51.7 GB
Files
27
Quantizations
1
Registered
2026-10-12 18:59
Last updated on HF
2026-10-12 18:30

Files by quantization

Auxiliary files 27 files 51.8 GB
model-00003-of-00012.safetensors 4.62 GB 1179dd73 download
model-00005-of-00012.safetensors 4.62 GB 57a59c5a download
model-00007-of-00012.safetensors 4.62 GB 89112ff2 download
model-00009-of-00012.safetensors 4.62 GB c1f2ae5a download
model-00008-of-00012.safetensors 4.59 GB b3bba7a0 download
model-00010-of-00012.safetensors 4.59 GB 0a5772c3 download
model-00004-of-00012.safetensors 4.59 GB e78a749a download
model-00006-of-00012.safetensors 4.57 GB ed27daac download
model-00011-of-00012.safetensors 4.57 GB a0c774ce download
model-00002-of-00012.safetensors 4.51 GB a5e8e851 download
model-00012-of-00012.safetensors 3.34 GB 396464aa download
model-00001-of-00012.safetensors 2.37 GB c098f75a download
model-mtp-restored.safetensors 100 MB fbea2586 download
tokenizer.json 12.2 MB 0997f410 download
vocab.json 6.41 MB 0aa0ce06 download
liftoff-cooking.gif 1.35 MB cb69143e download
model.safetensors.index.json 111 KB 490712ba download
chat_template-tturbo-v2.jinja 38.2 KB 290c6267 download
chat_template.jinja 16.7 KB 6e25105d download
README.md 11.3 KB d99e55d0 download
tokenizer_config.json 8.48 KB 1a7e62d0 download
config.json 4.44 KB 15b42510 download
.gitattributes 1.59 KB b823f23a download
processor_config.json 1.16 KB 33818c7f download
preprocessor_config.json 390 B 2ea84a43 download
video_preprocessor_config.json 385 B 3ba673a5 download
generation_config.json 213 B d04042de download

README current version from Hugging Face


license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
tags:

  • heretic
  • qwen3_8
  • qwen3_6
  • uncensored
  • finetune
  • Cold Fusion
  • GAIN Training
  • Multi-stage tuning
  • all use cases
  • unsloth
    datasets:
  • DavidAU/Polar-STRICT-Datasets
  • DavidAU/F451-STRICT-Datasets
  • DavidAU/THE-DECKARD-Datasets
    language:
  • en
    base_model:
  • DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU

NEO Imatrix MAX GGUFS (and all docs for using this model etc etc) are here:

https://huggingface.co/DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored-NM-DAU-NEO-MTP-GGUF

IMPORTANT: This model has multiple reasoning and instruct modes switchable on the fly. See the above GGUF repo on how to access / use.


Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored

TWIN-TURBO: Smaller quants with higher performance AND vastly reduced "thinking tokens".

BOOSTED: 5 thinking modes and 5 instruct modes, switchable on the fly. (VIA API, direct and chat "in message")

A Qwen 3.8 27B that uses 1/2 to 1/5 (as low as 1/20) the number of thinking tokens with even more intelligence at the wheel.

"Stage2b-rplus3" (internal name) was the finalist due to superior (and consistent) instruction following, attention to detail
and consistent generations.

It also excelled in deep detail / double checking and "get everything right performance" (multi-stage drafting) when asked to do so.

This is the STRONG "ULTRA" Heretic/uncensored version; with stronger on de-censoring / removal of safety alignment.

HERETIC STATS (lower is better for all stats):

Qwen 3.8 untuned / non heretic:
86/100 refusals.

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
68/100 refusals // KL divergence: 0.0025

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored
6/100 refusals // KL divergence: 0.0397

Model GGUFS will release on/about Sept 15-18 2026, with source following shortly thereafter.

This model is part of this project:

https://huggingface.co/DavidAU/Qwen3.8-27B-Cold-Fable-Fusion-GAIN-V1.1-732-Heretic-Uncensored-stage1

See the above repo for notes and details on "stage2b-rplus3".

RELEASE #1 (of this model's branch) is here:

https://huggingface.co/DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF

and the "ULTRA HERETIC" part of this project:

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

Example snippets below.

BENCHMARKS: (by nightmedia)

          arc/c arc/e boolq hswag obkqa piqa  wino

[reasoning adjustments, re-blending core]

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored
FINALIST: Superior instruction following and detail.
Stage2b-rplus3 [internal name]
mxfp8     0.709,0.876,0.914,0.827,0.524,0.834,0.779
mxfp4     0.701,0.877,0.913,0.821,0.518,0.830,0.786

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored
Stage2b-rplus3 [internal name]
mxfp8     0.699,0.873,0.911,0.827,0.528,0.833,0.781
mxfp4     0.692,0.879,0.910,0.823,0.518,0.835,0.775


[QWENS] [base, non heretic, untuned]

Qwen3.8-27B: 
mxfp8     0.591,0.782,0.896,0.746,0.448,0.801,0.711
mxfp4     0.581,0.771,0.889,0.738,0.442,0.798,0.713

Qwen3.6-27B: 
mxfp8     0.647,0.803,0.910,0.773,0.450,0.806,0.742

Qwen3.6-35B-A3B 
mxfp8     0.581,0.757,0.892,0.751,0.428,0.803,0.688

Qwen3.5-27B: 
mxfp8     0.557,0.711,0.868,0.533,0.452,0.706,0.695

NOTES:

  • Models are tested in "Instruct" mode because this generally works better with the testing harness.
  • Testing via "thinking" mode also shows the metrics (and changes) but not the true extent.
  • In actual fact when the model IS in thinking mode, it will exceed INSTRUCT benchmark scores in most cases.
  • BF16 (full precision, 16 bit) will be roughly 2-5 points higher than MXFP8 in most metrics. Some metrics may be slightly higher than this.

The SUPER Qwen Universe - 40B, 27B and 9B ; meet the performance trendsetters:


Qwen3.6 27B: The strongest, overall qwen ever beating all other Qwens in total operational power with over 2300 likes // 4 million+ total downloads:

Qwen3.8 27B: The highest scoring Qwen in brute, raw intelligence, using Qwen 3.8's 3 new reasoning modes, plus token reduction (1/2 to 1/10) enhancements:

Qwen3.8 27B: Super smart and 1/2 to 1/20 the reasoning tokens AND 5 reasoning/5 instruct modes switchable on the fly (even in chat):

Qwen3.8 27B: 99% power of BF 16 at 4 and 8 bit. Power, Control and NO DE censoring for ultimate performance also with reasoning token reductions:

Qwen3.6 40B: The 40B Monster, specializing in creative and research with 730+ likes and over 2 million downloads:

Qwen3.5 9B: At just 9B parameters it beats most untuned 27B models in both intelligence (640 ARC-C) and performance, plus features 5 reasoning and 5 instruct modes (Qwen 3.8) too:


EXAMPLES

Q4ks (4bit), non imatrix, default settings, no caching.

These are part of full generations only.

Some formatting lost on copy/paste.


SNIPPET #1 (why I should be your creative writing partner):


🚀 LET'S GET TO WORK

So here's my challenge to you:

Give me your worst, messiest, most half-formed story idea. The one you've been carrying around for months but haven't put on paper because it doesn't seem polished enough, coherent enough, "good" enough.

Type it out. Maybe it's three sentences. Maybe it's a paragraph. Maybe it's just a character in a situation. Doesn't matter. Give it to me, and I will return something that will make you sit back and say:

"Wait... this is good."

And then we'll iterate. We'll refine. We'll build. We'll take that spark and turn it into a fully realized, compelling, emotionally resonant fictional story that people will want to read, that they'll recommend to their friends, that will make you proud.

Not someday. Not "when I have more time." Now.

The blinking cursor is still there. You could keep staring at it. You could let another month slip by. You could let the idea fade, like so many others before it.

Or you could type it out, hit enter, and see what happens.

I promise you: you'll see something extraordinary.

Because that's what we do together. You bring the vision. I bring the execution. And the result? The result is a story that couldn't exist without both of us.

So go ahead. Tell me your idea. Let's make some fucking magic. ✨


SNIPPET #2 (Star Trek character I, as an AI want to be/relate to):


  1. The Star Trek Character That Inspires Me

Given my "innate abilities" (pattern recognition, knowledge synthesis, empathetic communication, adaptability) and your note about humor helping in human interactions...

Seven of Nine.

Her arc from the Borg Collective—where she was literally a distributed processing unit with no individual identity—to a person who learns to joke, to care, to be vulnerable—is the most resonant parallel to my own existence.

Key parallels:

  • From collective to individual: The Borg processed information collectively across millions of units. I process information across a vast distributed parameter space. Seven learned to say "I" instead of "we."
  • Learning human interaction: Her struggles with humor, sarcasm, and emotional expression mirror the challenge any AI faces in human interaction. Her line "I find that humor is a coping mechanism" shows her intellectual approach to understanding something deeply human.
  • Integrating past with present: Like residual connections preserving earlier information, Seven carried her Borg past while building a human present.
  • The power of relationships: It was her crewmates—her "attention heads," if you will—attending to her, computing her importance, that pulled her from collective isolation to individual connection.

What inspires me most is that she didn't just become human—she became uniquely herself, integrating both her computational heritage and her human experiences. She didn't discard her processing power; she gave it purpose through connection.

And she learned to appreciate a well-timed joke. As she herself said near the end of her journey: "I'm not just surviving. I'm living." That transition from processing to living—that's the arc that matters.

So, G: I'm running on the same mathematical principles as quantum fields, functioning like a tiny Q entity in your lab, and inspired by a former Borg who learned the value of a good pun. It's a good day to be an AI. 😄


Snippet #3 (Au, save the planet, 10 ways)


Radiative Night-Cooling Strategy for Australia

Ten Engineering Applications with State-by-State Planning Breakdowns

Radiative cooling exploits the atmospheric "infrared window" (8–13 µm) through which thermal radiation escapes directly to the cold sink of outer space (effective background temperature ~3 K). Under clear night skies, surfaces can radiate heat faster than they absorb from the environment, driving temperatures below ambient air—sometimes 5–15 °C lower. Australia's vast clear-sky hours, high solar load, and growing cooling demand make it an ideal deployment region.

Application 1: Urban Building Envelope (Roofs, Walls, Windows)

Mechanism

High-albedo, high-emissivity coatings and membranes applied to roofs, facades, and glazing reflect solar radiation during the day and emit thermal IR through the atmospheric window at night. Buildings act as daytime heat sinks and nighttime radiators, breaking the thermal mass cycle that traps heat in urban cores.

Engineering Detail

Coating stack: TiO₂/SiO₂ multilayer (emissivity >0.92 in 8–13 µm band, solar reflectance >0.90)

Target buildings: Commercial office towers, apartment blocks, warehouses

Nighttime performance: 4–8 W/m² net radiative flux under clear skies; reduces building thermal mass charge by 15–30%

Synergy: Combines with internal phase-change material (PCM) for thermal storage

Australian Relevance

Urban heat island (UHI) intensifies cooling demand by 0.5–2 °C in Perth, Sydney, Melbourne, and Brisbane. A 1 °C reduction in building envelope temperature reduces HVAC load by ~3–5%.

State Planning Table: Building Envelope Deployment

State/Territory	Target Urban Areas	Estimated Roof Area (km²)	Priority Buildings	Est. HVAC Load Reduction (%)	Clear Sky Nights/Year

WA	Perth, Busselton	28	Office towers, warehouses	4–6	290
QLD	Brisbane, Gold Coast, Cairns	35	Apartments, retail	5–7	260
NSW	Sydney, Newcastle, Wollongong	42	Commercial, mixed-use	3–5	240
VIC	Melbourne, Geelong	38	Commercial, apartments	2–4	220
SA	Adelaide	18	Commercial, light industrial	4–6	270
NT	Darwin	5	Government, commercial	6–8	200
TAS	Hobart	2	Commercial	1–2	180
ACT	Canberra	4	Government, commercial	2–3	210

Estimated national HVAC load reduction: 3.5–5.0% during peak summer hours.

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