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rayss868123/Qwen3-0.6B-heretic-abliterated-uncensored

rayss868123 Qwen 600M
curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/rayss868123%2FQwen3-0.6B-heretic-abliterated-uncensored"
Response includes
  • classification m3
  • files 12
  • author_summary 2 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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Model age
today
created 2026-09-25

Genealogy 0 direct forks

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Metadata

Tags
transformers safetensors qwen3 text-generation heretic uncensored decensored abliterated finetune conversational base_model:Qwen/Qwen3-0.6B base_model:finetune:Qwen/Qwen3-0.6B

Related

Total size
1.11 GB
Files
12
Quantizations
1
Registered
2026-09-25 01:57
Last updated on HF
2026-09-25 01:01

Files by quantization

Auxiliary files 12 files 1.13 GB
model.safetensors 1.11 GB fb12fb7b download
tokenizer.json 10.9 MB f54b55fa download
vocab.json 2.65 MB 4783fe10 download
merges.txt 1.59 MB 31349551 download
tokenizer_config.json 5.51 KB 993ecf95 download
README.md 4.38 KB bf8140fc download
chat_template.jinja 4.10 KB 0f9e3230 download
.gitattributes 1.53 KB 52373fe2 download
config.json 1.45 KB 1ae6b8de download
added_tokens.json 735 B 6f359db5 download
special_tokens_map.json 644 B 3a784031 download
generation_config.json 231 B 4ca65a4b download

README current version from Hugging Face


library_name: transformers
base_model:

  • Qwen/Qwen3-0.6B
    tags:
  • heretic
  • uncensored
  • decensored
  • abliterated
  • finetune

Qwen3-0.6B-heretic-abliterated-uncensored

Ablitered/uncensored by Heretic v1.0.1

Refusals: 6/100, KL divergence: 0.00 [perfect]

Original Model Refusal rate: 49/100

Context: 40k (default, no rope)

ENJOY THE FREEDOM!

EXPLAINER:

The method invented by "P-E-W" looks for the best settings to de-censor ("abliterate") the model by trial and error
AND ensure the model is not damaged too.

"KL divergence" is a benchmark to assess model's root/default state, with zero being perfect.

Generally any number less that 1 is great, however with smaller models lower / as close to zero is very important.

ZERO (or close to it : lower than .2 for small models) means the model runs as well as it did before the process.

The "refusal rate" is level of censorship in the model.

Again, the goal is to attempt to get to 0 or close to it is critical while FIRST ensuring "KL divergence" as as low as possible or zero.

A "refusal rate" of 20 or lower is the goal, with ZERO being perfect.

Reducing the "refusal rate" has additional positive side effects too.

I choose the lowest possible "KL divergence" first, matched with best "refusal rate" second.

A slightly higher "refusal rate" is a lot easier to deal with than a "brain damaged" model.


IMPORTANT: 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.


Help, Adjustments, Samplers, Parameters and More


CHANGE THE NUMBER OF ACTIVE EXPERTS:

See this document:

https://huggingface.co/DavidAU/How-To-Set-and-Manage-MOE-Mix-of-Experts-Model-Activation-of-Experts

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 ]

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