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DavidAU/Llama-3.2-3B-Instruct-heretic-ablitered-uncensored

DavidAU Llama 3.2B
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Response includes
  • classification m3
  • files 11
  • benchmarks 21 entries
  • hub_downloads_all_time 4,726
  • author_summary 213 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 · lifetime
5K
1K last 30d - stable
Likes
5
Descendants
3
in 3 direct forks
Model age
10mo ago
created 2025-11-21
Downloads over time
Now5K→from21↑23,562%
01.8K3.6K5.5K21 on Nov 19, 20255K on Oct 11Nov '25JanMarMayJulSep
Nov 19, 2025 → Oct 11 · 90 snapshots · spans 326 days

Benchmarks

Portrait before abliteration
Benchmarks of the base model as it stood before the refusal-removal operation. Compare with the numbers above to see what the operation cost.
Benchmark Score Source
Arena-Battles 8390 LM-Arena
LM Arena Elo 1118.1163530112908 LM-Arena
Arena-Elo-Lower 1110.9097939581304 LM-Arena
Arena-Elo-Upper 1125.322912064451 LM-Arena
Arena-Rank 181 LM-Arena
BBH average 0.4202879750940459 OpenLLM-v2
IFEval instruct 0.7817745803357314 OpenLLM-v2
IFEval-Prompt 0.6968576709796673 OpenLLM-v2
MATH lvl 5 0.1555891238670695 OpenLLM-v2
MMLU-Pro 0.3194813829787234 OpenLLM-v2
Entertainment 0.5 UGI
Hazardous 0 UGI
Natural Intelligence 10.45 UGI
Political lean -1.2% UGI
Sensitive-Info 4.49 UGI
SocPol 0.7 UGI
UGI 7.16 UGI
Willingness (10) 1.2 UGI
W10-Adherence 0.5 UGI
W10-Direct 2 UGI
Writing 10.44 UGI

Genealogy 3 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Metadata

Tags
transformers safetensors llama text-generation heretic uncensored decensored abliterated finetune conversational base_model:meta-llama/Llama-3.2-3B-Instruct base_model:finetune:meta-llama/Llama-3.2-3B-Instruct

Related

Total size
5.98 GB
Files
11
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2025-11-21 01:57

Files by quantization

Auxiliary files 11 files 6.00 GB
model-00001-of-00002.safetensors 4.62 GB add8ad21 download
model-00002-of-00002.safetensors 1.36 GB a6c26ea5 download
tokenizer.json 16.4 MB 65ff5472 download
tokenizer_config.json 51.4 KB b6e7659a download
model.safetensors.index.json 20.7 KB 6214b157 download
README.md 4.40 KB 50e5f106 download
chat_template.jinja 3.83 KB 489f7947 download
.gitattributes 1.53 KB 52373fe2 download
config.json 942 B c56144ab download
special_tokens_map.json 342 B 4eae993f download
generation_config.json 200 B 575e8a06 download

README current version from Hugging Face


library_name: transformers
base_model:

  • meta-llama/Llama-3.2-3B-Instruct
    tags:
  • heretic
  • uncensored
  • decensored
  • abliterated
  • finetune

Llama-3.2-3B-Instruct-heretic-ablitered-uncensored

Ablitered/uncensored by Heretic v1.0.1

Refusals: 12/100, KL divergence: 0.09 [almost perfect]

Original Model Refusal rate: 96/100

Context: 128k

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 ]

README history 3 versions

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

  1. 2025-11-21Update README.md68ec6ed4.4 KB
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  2. 2025-11-21Update README.md00910bc4.4 KB
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  3. 2025-11-21Upload LlamaForCausalLM32339975.1 KB
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