Heretic: Automatic censorship removal for language models

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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), 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.

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.)

Heretic supports most dense models, including many multimodal models, and several different MoE architectures. It does not yet support SSMs/hybrid models, models with inhomogeneous layers, and certain novel attention systems.

You can find a collection of models that have been decensored using Heretic on Hugging Face.

Prepare a Python 3.10+ environment with PyTorch 2.2+ installed as appropriate for your hardware. Then run:

pip install heretic-llm heretic Qwen/Qwen3-4B-Instruct-2507

Replace Qwen/Qwen3-4B-Instruct-2507 with whatever model you want to decensor.

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 Llama-3.1-8B takes about 45 minutes.

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, or any combination of those actions.

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 "refusal direction", inhibiting the expression of that direction in the result of multiplications with that matrix.

Refusal 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 refusal direction, or the special value per layer, indicating that each layer should be ablated using the refusal 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 refusal direction index is a float rather than an integer. For non-integral values, the two nearest refusal 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.

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.

The development of Heretic was informed by:

The original abliteration paper (Arditi et al. 2024)
Maxime Labonne's article on abliteration, as well as some details from the model cards of his own abliterated models (see above)
Jim Lai's article describing "projected abliteration"

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/.

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