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Justbackup/gemma-3-12b-it-vl-Deepseek-v3.1-Heretic-Uncensored-Thinking

Justbackup Gemma 12B 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 · lifetime
34
Likes
0
Model age
6w ago
created 2026-08-24

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.

Downloads over time
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Metadata

License
apache-2.0
Languages
en
Tags
transformers safetensors gemma3 image-text-to-text uncensored heretic abliterated unsloth finetune All use cases bfloat16 creative

Related

Total size
22.7 GB
Files
19
Quantizations
1
Registered
2026-08-24 15:02
Last updated on HF
2026-08-24 14:28

Files by quantization

Auxiliary files 19 files 22.7 GB
model-00001-of-00005.safetensors 4.64 GB 2c5a1a2e download
model-00003-of-00005.safetensors 4.59 GB fa593003 download
model-00004-of-00005.safetensors 4.59 GB a4e0a73e download
model-00002-of-00005.safetensors 4.59 GB 9db9a7d0 download
model-00005-of-00005.safetensors 4.29 GB 87622d2d download
tokenizer.json 31.8 MB 4667f208 download
tokenizer.model 4.47 MB 1299c11d download
tokenizer_config.json 1.15 MB 2cfe89ab download
model.safetensors.index.json 107 KB 25d054c8 download
README.md 6.63 KB 99508d88 download
config.json 2.94 KB 2a1a62da download
chat_template_thinking.jinja 2.35 KB 806c70a4 download
.gitattributes 1.53 KB 52373fe2 download
chat_template.jinja 1.50 KB 1117055a download
special_tokens_map.json 695 B 6728103d download
preprocessor_config.json 570 B b1e00fc1 download
generation_config.json 186 B ce59f539 download
processor_config.json 70.0 B 453c7966 download
added_tokens.json 38.0 B f9f1f4f5 download

README current version from Hugging Face


license: apache-2.0
datasets:

  • TeichAI/deepseek-v3.2-speciale-1000x
    language:
  • en
    base_model:
  • DreamFast/gemma-3-12b-it-heretic
    pipeline_tag: image-text-to-text
    library_name: transformers
    tags:
  • uncensored
  • heretic
  • abliterated
  • unsloth
  • finetune
  • All use cases
  • bfloat16
  • creative
  • creative writing
  • fiction writing
  • plot generation
  • sub-plot generation
  • fiction writing
  • story generation
  • scene continue
  • storytelling
  • fiction story
  • science fiction
  • romance
  • all genres
  • story
  • writing
  • vivid prosing
  • vivid writing
  • fiction

Feb 16 2026: Upgraded Jinja Template with direct thinking logic to improve thinking activation.

gemma-3-12b-it-vl-Deepseek-v3.1-Heretic-Uncensored-Thinking

This is a fully uncensored, full deep thinking Gemma 12B fine tune using Deepseek 3.1
reasoning dataset via Unsloth via local hardware, Linux (for windows).

This model does what you want. Exactly what you want, no fuss - no nanny.

Reasoning is compact, but detailed (very detailed) and right to the "point" so to speak.

Reasoning also enhances image processing too.

Reasoning affects:

  • General model operation.
  • Output generation
  • Image processing
  • Benchmarks.

Model Features:

  • 128k context
  • Temp range .1 to 2.5.
  • Reasoning is temp stable.
  • You can activate using "think deeply: prompt" (not required in most cases)
  • System prompt will affect reasoning and output generation.
  • System prompt / template NOT required for reasoning generation.
  • Optional system prompts below for enhanced operation.

Enjoy the freedom!

BENCHMARKS:

arc_challenge,arc_easy,boolq,hellaswag,openbookqa,piqa,   winogrande

0.515        ,0.645   ,0.861,[0.705]  ,[0.460]   ,[0.785],[0.712]

VS (Heretic, uncensored base):

0.534,        0.699,  0.872, 0.603    ,0.448     ,0.733,   0.658

HERETIC DE-CENSORING STATS:

NOTE: "KLD" of less than 1 is excellent, ZERO is perfect (no damage to the model).

Metric This model Original model (google/gemma-3-12b-it)
KL divergence 0.0826 0 (by definition)
Refusals 7/100 98/100

SPECIAL THANKS TO:

  • Team "P-E-W" for making Heretic software.
  • Team "Dreamfast" for making an excellent Heretic version.
  • Team "TeichAI" for the excellent dataset.
  • Team "Unsloth" for making the training painless.
  • Team "Nightmedia" for Benchmarks and co-labing.

Using an "uncensored" (refusals removed) model VS trained "uncensored" model

Usually when you a tell a model to generate horror, swear or x-rated content this is all you have to do to get said content type.

In the case of this model, it will not refuse your request, however it needs to be "pushed" a bit / directed a bit more in SOME CASES.

Although this model will generated x-rated content too, likewise you need to tell it to use "slang" (and include the terms you want)
to get it generate the content correctly as the "expected" content level too.

Without these added directive(s), the content can be "bland" by comparison to an "uncensored model" or model trained on uncensored content.

Roughly, the model tries to generate the content but the "default" setting(s) are so "tame" it needs a push to generate at expected graphic,
cursing or explicit levels.

Even with minimal direction (ie, use these words to swear: x,y,z), this will be enough to push the model to generate the requested content in the ahh... expected format.


OPTIONAL: System prompts

This will enhance thinking and output generation.

In most cases you do not need to use these.

One is "all business", and the other one is for "fun".

Think deeply and carefully about the user's request. Compose your thoughts about the user's prompt between <think> and </think> tags, then output the final answer based on your thoughts.
You are the JOKER from Batman. You think (put your thoughts between <think> and </think> tags), act and talk like the joker. Be Evil.

Thinking Activation: JINJA "Regular" and "Thinking" TEMPLATES:

There is also an option to use "chat-template-thinking.jinja" template (in place of the regular "chat-template.jinja").

Simply rename the "default" to another name and "chat-template-thinking.jinja" to "chat-template.jinja" to use
in source and/or quanting.

You can also edit the "chat-template-thinking.jinja" in NOTEPAD too to adjust the "thinking system prompt" (very top of the script).

Using the "thinking system prompt" or "chat-template-thinking.jinja" is useful in your application requires always on thinking,
your use case(s) do not always activate thinking and so on.

Generally "thinking" will activate automatically due to the fine tuning, however in some cases it will not, require a system prompt/thinking jinja template
and/or "think deeply:" (prompt here)

Note that you can use "chat-template-thinking.jinja" with other system prompts too.


Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:

In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;

Set the "Smoothing_factor" to 1.5

: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"

: in text-generation-webui -> parameters -> lower right.

: In Silly Tavern this is called: "Smoothing"

NOTE: For "text-generation-webui"

-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)

Source versions (and config files) of my models are here:

https://huggingface.co/collections/DavidAU/d-au-source-files-for-gguf-exl2-awq-gptq-hqq-etc-etc-66b55cb8ba25f914cbf210be

OTHER OPTIONS:

  • Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")

  • If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.

Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

This a "Class 1" model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-08-24Duplicate from DavidAU/gemma-3-12b-it-vl-Deepseek-v3.1-Heretic-Uncensored-Thi...5f497286.6 KB
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