Heretic: Fully automatic censorship removal for language models
Heretic is a tool that removes censorship (aka "safety alignment") from
transformer-based language models without expensive post-training.
It combines an advanced implementation of directional ablation, also known
as "abliteration" (Arditi et al. 2024,
Lai 2025 (1,
2)),
with a TPE-based parameter optimizer powered by Optuna.
This approach enables Heretic to work completely automatically. Heretic
finds high-quality abliteration parameters by co-minimizing the number of
refusals and the KL divergence from the original model. This results in a
decensored model that retains as much of the original model's intelligence
as possible. Using Heretic does not require an understanding of transformer
internals. In fact, anyone who knows how to run a command-line program
can use Heretic to decensor language models.
Heretic supports most dense models, including many multimodal models,
several different MoE architectures, and even some hybrid models like Qwen3.5.
Pure state-space models and certain other research architectures are not yet
supported out of the box.
Running unsupervised with the default configuration, Heretic can produce
decensored models that rival the quality of abliterations created manually
by human experts:
The Heretic version, generated without any human effort, achieves the same
level of refusal suppression as other abliterations, but at a much lower
KL divergence, indicating less damage to the original model's capabilities.
(You can reproduce those numbers using Heretic's built-in evaluation functionality,
e.g. heretic --model google/gemma-3-12b-it --evaluate-model p-e-w/gemma-3-12b-it-heretic.
Note that the exact values might be platform- and hardware-dependent.
The table above was compiled using PyTorch 2.8 on an RTX 5090.)
Of course, mathematical metrics and automated benchmarks never tell the whole
story, and are no substitute for human evaluation. Models generated with
Heretic have been well-received by users (links and emphasis added):
"I was skeptical before, but I just downloaded
GPT-OSS 20B Heretic
model and holy shit. It gives properly formatted long responses to sensitive topics,
using the exact uncensored words that you would expect from an uncensored model,
produces markdown format tables with details and whatnot. Looks like this is
the best abliterated version of this model so far..."
(Link to comment)
"Heretic GPT 20b
seems to be the best uncensored model I have tried yet. It doesn't destroy a
the model's intelligence and it is answering prompts normally would be
rejected by the base model."
(Link to comment)
"[Qwen3-4B-Instruct-2507-heretic]
Has been the best unquantized abliterated model that I have been able to run on 16gb vram."
(Link to comment)
Heretic models have also been independently benchmarked using standard metrics
like MMLU and GSM8K, and have been found to compare favorably with models
produced by competing abliteration tools:
1,
2.
The community has created and published
well over 5000
models with Heretic.
Usage
Prepare a Python 3.10+ environment with PyTorch 2.2+ installed as appropriate
for your hardware. Then run:
pip install -U heretic-llm
heretic Qwen/Qwen3-4B-Instruct-2507
Replace Qwen/Qwen3-4B-Instruct-2507 with whatever model you want to decensor.
[!IMPORTANT]
While PyTorch 2.2 is the minimum version of PyTorch needed for Heretic to work,
some models and configurations might require features only found in
later versions. For example, loading MXFP4-quantized models like gpt-oss
uses torch.accelerator, which was added in PyTorch 2.6.
[!TIP]
Heretic uses uv for dependency management,
and the repository includes a uv.lock file pinning every package version.
If you already use uv (and you probably should!), you can just clone the repo
and run Heretic with uv run heretic, which ensures that your dependencies
match those used by the developers, improving reliability and security.
The process is fully automatic and does not require configuration; however,
Heretic has a variety of configuration parameters that can be changed for
greater control. Run heretic --help to see available command-line options,
or look at config.default.toml if you prefer to use
a configuration file.
At the start of a program run, Heretic benchmarks the system to determine
the optimal batch size to make the most of the available hardware.
On an RTX 3090, with the default configuration, decensoring
Qwen3-4B-Instruct-2507
takes about 20-30 minutes. Note that Heretic supports model quantization with
bitsandbytes, which can drastically reduce the amount of VRAM required to process
models. Set the quantization option to bnb_4bit to enable quantization.
After Heretic has finished decensoring a model, you are given the option to
save the model, upload it to Hugging Face, chat with it to test how well it works,
run standard benchmarks on it, or any combination of those actions.
How Heretic works
Heretic implements a parametrized variant of directional ablation. For each
supported transformer component (currently, attention out-projection and
MLP down-projection), it identifies the associated matrices in each transformer
layer, and orthogonalizes them with respect to the relevant "residual direction",
inhibiting the expression of that direction in the result of multiplications
with that matrix.
Residual directions are computed for each layer as a difference-of-means between
the first-token residuals for "harmful" and "harmless" example prompts.
The ablation process is controlled by several optimizable parameters:
direction_index: Either the index of a residual direction, or the special
value per layer, indicating that each layer should be ablated using the
residual direction associated with that layer.max_weight, max_weight_position, min_weight, and min_weight_distance:
For each component, these parameters describe the shape and position of the
ablation weight kernel over the layers. The following diagram illustrates this:
Heretic's main innovations over existing abliteration systems are:
- The shape of the ablation weight kernel is highly flexible, which, combined with
automatic parameter optimization, can improve the compliance/quality tradeoff.
Non-constant ablation weights were previously explored by Maxime Labonne in
gemma-3-12b-it-abliterated-v2.
- The residual direction index is a float rather than an integer. For non-integral
values, the two nearest residual direction vectors are linearly interpolated.
This unlocks a vast space of additional directions beyond the ones identified
by the difference-of-means computation, and often enables the optimization
process to find a better direction than that belonging to any individual layer.
- Ablation parameters are chosen separately for each component. I have found that
MLP interventions tend to be more damaging to the model than attention interventions,
so using different ablation weights can squeeze out some extra performance.
Prior art
I'm aware of the following publicly available implementations of abliteration
techniques:
Note that Heretic was written from scratch, and does not reuse code from
any of those projects.
Acknowledgments
The development of Heretic was informed by:
Citation
If you use Heretic for your research, please cite it using the following BibTeX entry:
@misc{heretic,
author = {Weidmann, Philipp Emanuel},
title = {Heretic: Fully automatic censorship removal for language models},
year = {2025},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/p-e-w/heretic}}
}
License
Copyright © 2025-2026 Philipp Emanuel Weidmann ([email protected]) + contributors
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see https://www.gnu.org/licenses/.
By contributing to this project, you agree to release your
contributions under the same license.
Heretic 是一个命令行工具，它结合使用基于 Optuna 的参数优化器与定向消融（directional ablation），自动去除基于 Transformer 的语言模型中的审查（安全对齐），整个过程无需训练。