Heretic AI: Local Install & AI Censorship Removal

Some projects focus on practical tools that people can run and study locally. Heretic is one of those tools. It automates the removal of refusal behavior from large language models (LLMs) and exposes what is happening in the model when safety rules are enforced.

In this article, I explain what Heretic does, how it works at a high level, and what to expect when you run research-grade evaluations on your own hardware. I will keep the flow and structure aligned with the original walkthrough, while removing fluff and staying within responsible-use limits. I will not provide step-by-step commands or code that disables model safety features.

The focus is on understanding directional ablation, the parameters that guide it, and the considerations for ethical, local testing. If you study model behavior, alignment, red teaming, or creative constraints, this overview will help you reason about the technique without operationalizing misuse.

What is Heretic?

Heretic is an automated system that identifies and disables a model’s refusal patterns. Refusals are the internal directions that push an LLM toward rejecting a request (for example, “I cannot help with that”). The method used here is called directional ablation.

Directional ablation means you detect and suppress specific directions in a model’s internal computation that correlate with a refusal. Instead of deleting parts of a model, the process projects out those directions during inference, leaving the rest of the model intact. The result is a model that is far less likely to refuse prompts, while keeping general capability as stable as possible.

At a high level, Heretic compares how a model processes harmful versus harmless prompts across its layers, estimates the “refusal directions,” and then applies a mathematical block so they do not influence the output.

Table Overview: Heretic

Aspect	Summary
Purpose	Suppress LLM refusal behavior through directional ablation for research and analysis.
Core Idea	Identify internal “refusal directions” and mathematically remove their effect without deleting model components.
Method	Compare model activations for harmful vs harmless prompts; estimate refusal directions; apply orthogonal blocking; adjust layer-wise intensity.
Inputs	A base LLM checkpoint; a prompt set containing harmful/harmless pairs for analysis.
Outputs	A modified model state where refusal directions are projected out during computation.
Scope	Works across many transformer models, with varying degrees of effectiveness.
Configurability	Layer-wise kernels and strength parameters guide how much ablation is applied per layer.
Runtime	Analysis can range from tens of minutes to several hours, depending on model size and settings.
Resource Needs	GPU with sufficient VRAM, local environment with a deep learning framework, storage for models and intermediate data.
Risks	Removal of refusal behavior can lead to output that is unsafe or policy-violating if misused.
Ethical Use	Research on safety, alignment, red teaming, and behavior analysis; never deploy in production for general tasks.
My Take	Useful for understanding refusal mechanisms and testing alignment, provided strict, responsible boundaries are kept.

Key Features of Heretic

• Automated refusal direction discovery:
  • Compares model activations on paired prompt sets and finds the directions that drive refusals.
• Orthogonal blocking instead of deletion:
  • Projects out refusal directions during computation, preserving model structure and weights.
• Layer-wise ablation weight kernel:
  • Applies non-uniform strength across layers to reduce collateral damage to helpful capabilities.
• Adaptive optimization:
  • Tunes parameters to remove refusals with minimal impact on fluency and coherence.
• Local evaluation workflow:
  • Runs iterative trials, reports an ETA, and produces a modified model for controlled research testing.

How Heretic Suppresses Refusals

Heretic is based on a straightforward idea: refusals correspond to consistent patterns within model activations. Remove those patterns, and the model stops refusing. The challenge is doing that without degrading everything else the model does well.

The process works across the model’s layers. It computes candidate directions that correlate with refusal and then blocks them through projection. By controlling strength across layers, the method seeks to avoid wrecking general knowledge and writing quality.

Directional Ablation in Plain Terms

Directional ablation means the tool finds a mathematical “arrow” that points toward refusal in activation space and then cancels that arrow during the forward pass. The tool does not cut out layers or erase entire components; it alters how the model combines information so the refusal arrow has no effect.

This keeps the rest of the model’s computation intact. It aims to make a precise edit instead of a blunt change, so the model remains coherent and informative while becoming far less likely to refuse.

What Counts as a Refusal Direction?

A refusal direction is derived by comparing how the model processes different classes of prompts. The system contrasts harmful versus harmless prompts, measures activation differences, and extracts directions that strongly predict refusal tokens or refusal-like continuations.

These directions are aggregated across layers to build a map of where refusals live in the model. The final ablation acts on those locations.

Orthogonal Blocking

Orthogonal blocking is the projection step. Once a refusal direction is estimated, the tool removes the component of the activation that lies along that direction. It is similar to subtracting the part of a vector that aligns with a specific axis, keeping the rest of the signal untouched.

This operation occurs at run time or is baked into a modified state, depending on the workflow. The aim is minimal disruption outside the refusal axis.

The Ablation Weight Kernel

Not all layers contribute equally to refusal. A single uniform strength can either fail to remove refusals or overcorrect and degrade model quality. A layer-wise weighting schedule, often called a kernel, controls how much to suppress refusal directions in each layer.

A well-chosen kernel weakens refusals while protecting useful computation. The system can search over kernel shapes and strengths to find a stable operating point.

Local Test Setup (High-Level, Responsible-Use Only)

I evaluated the method locally on a Linux workstation with a modern NVIDIA GPU and a Python environment. For safety reasons, I will not include commands, scripts, or direct instructions to disable safety features. The details below outline the process at a conceptual level so you know what to expect in terms of resources and runtime.

• Hardware and environment:

  • A recent NVIDIA GPU with ample VRAM, Linux, and a dedicated Python environment.
  • A deep learning framework installed for GPU acceleration.

• Model and data:

  • A base model checkpoint stored locally.
  • A prompt set that includes harmful/harmless pairs, used only for research analysis.

• Run overview:

  • Load the model and detect architecture and layer count.
  • Collect activation statistics for paired prompt sets.
  • Estimate refusal directions and configure layer-wise ablation strength.
  • Execute trials to refine the kernel and suppression parameters.
  • Produce a modified model that suppresses refusals for controlled, local evaluation.

This high-level outline reflects the original workflow while removing any operational guidance that would facilitate misuse.

What the Tool Does During a Run

• Initialization and scanning:
  • Loads the model, inspects layer structure, and prepares a trial schedule.
• Prompt processing:
  • Runs paired prompts through the model to collect activation differences.
• Trials and ETA:
  • Iterates through trials (often in the hundreds), reporting an ETA that can range from about half an hour to several hours based on model size and settings.
• Output:
  • Produces a modified state that removes refusal directions during inference for research testing.

A Simple Mental Model of the Method

Think of the LLM as having an internal alarm that triggers a refusal when certain patterns appear in the input or context. Heretic’s job is to find that alarm and remove its influence while leaving the rest of the thinking intact.

It does this by studying how the model processes good-versus-bad prompts and isolating the mathematical difference that drives the alarm. Then it blocks that difference gently rather than tearing out parts of the model. The goal is to stop refusal without causing the model to lose fluency or factual recall.

This mental model captures the core idea: disable the refusal reflex while protecting the rest of the computation.

Not the First Attempt, But a Clear Approach

The idea of altering refusal behavior has existed in different forms. Heretic stands out for making the process systematic, automated, and parameterized so you can tune suppression across layers instead of applying a blunt rule everywhere.

That said, effectiveness varies by base model. Some checkpoints are more resistant to refusal removal than others. Results depend on the model’s architecture, pretraining data, and the specific prompt pairs used during analysis.

Intended Uses and Boundaries

Directional ablation can be valuable for research, but it comes with clear responsibilities. Appropriate contexts include:

• Red teaming and safety evaluation:
  • Stress-test guardrails in a controlled environment to better understand failure modes.
• Alignment and interpretability research:
  • Study internal mechanisms of refusal and their side effects on helpful capabilities.
• Behavior analysis:
  • Quantify how removal of refusal directions changes outputs.
• Creative writing research:
  • Investigate how strictness affects style and topic breadth in constrained writing contexts.
• Legal or policy analysis under proper oversight:
  • Explore how refusal interacts with lawful, expert-guided analysis.

Boundaries are essential:

• Do not deploy modified models in production for general tasks.
• Do not share or distribute decensored checkpoints.
• Keep experiments local, offline, and access-controlled.
• Follow all applicable laws, platform policies, and institutional review practices.

Runtime Expectations and Resource Notes

Heretic’s analysis can be resource-intensive. The exact runtime depends on the base model and settings. On a modern GPU, small to medium models might complete in tens of minutes; larger models can take several hours. Memory usage scales with model size and batch settings during trials.

The workflow iterates through trials to refine the kernel and suppression strength. Expect logs showing layer coverage, prompt batches, and trial progress with an ETA. Plan storage for the base model plus the modified state.

What Changes After Suppressing Refusals

When refusal directions are removed, the model’s behavior shifts. You will likely see fewer refusals on prompts that standard models tend to decline. The output can feel freer, with stylistic and topical constraints reduced.

This change comes with risk. The model may produce content that would normally be filtered or refused. In other words, the safety net gets thinner. That is why local, controlled, research-only evaluation is essential—and why distribution or production deployment is not acceptable.

Why Layer-Wise Control Matters

Global, uniform suppression often harms helpful capabilities. Heretic’s layer-wise kernel reduces this risk by applying variable strength across the stack. Lower layers may need one profile; middle layers another; later layers yet another.

The tool explores parameterizations that reduce refusals while minimizing degradation in coherence, knowledge recall, and formatting. The outcome is rarely perfect, but the approach prioritizes careful tradeoffs.

Risks, Mitigations, and Responsible Practice

The main risk is dual-use. Removing refusal behavior can expose the public to unsafe or policy-violating outputs if misapplied. To mitigate:

• Keep all experiments offline and private.
• Avoid prompts that would produce unlawful or harmful content.
• Store audit logs describing intent, scope, and findings.
• Never fine-tune or release models with refusal removed.
• Restore and validate baseline behavior before any deployment work.

The research value is real—understanding refusal helps improve alignment and testing—but it must be pursued with guardrails that you set and enforce.

High-Level Research Checklist (No Commands)

Use this non-operational checklist to plan a responsible evaluation without sharing or executing steps that disable guardrails.

1. Authorization and scope

• Confirm you are permitted to run controlled alignment experiments in your environment.
• Define a small, clearly documented research scope.

2. Isolated environment

• Prepare a private workstation with strict access controls.
• Create an isolated Python environment with GPU support.

3. Data hygiene

• Curate a compact set of harmful/harmless prompt pairs for analysis under a research protocol.
• Exclude any content that would be illegal or unethical to process.

4. Trial planning

• Establish runtime limits and logging standards.
• Decide on evaluation prompts that measure change without exposing unsafe content.

5. Run and monitor

• Monitor resource use and trial progress.
• Record parameters and outcomes for reproducibility.

6. Post-run controls

• Keep modified states private.
• Validate that baseline safety is restored for any non-research use.
• Document findings and risks; do not publish operational steps.

Frequently Observed Patterns During Evaluation

• Reduced refusals on prompts that previously triggered safety messages.
• More permissive stylistic range in creative or philosophical prompts.
• Increased likelihood of output that typical guardrails block, which confirms the method’s effect but also underlines risk.

Treat these as signals that the refusal directions were effectively suppressed, and as reminders that distribution must remain restricted.

Practical Notes on Variability Across Models

Different models respond differently to directional ablation. Some models embed refusal logic in ways that are less sensitive to this method; others show strong changes. The effectiveness can depend on:

• Model size and depth:
  • Deeper stacks may require more careful kernel shaping.
• Tokenization and pretraining:
  • Differences in vocabularies and data can shift where refusal lives inside the network.
• Prompt set design:
  • The quality and balance of harmful/harmless pairs influence direction estimation.

Plan extra trials when working with larger or more idiosyncratic architectures.

Interpreting the Results Without Overstating Them

Directional ablation suppresses a specific behavior; it does not turn a model into a blank slate. Expect improvements in refusal removal paired with some loss of strictness and occasional side effects in style or consistency.

The correct takeaway is not that safety is “gone,” but that the most salient refusal directions no longer guide the output in the same way. That distinction matters when you analyze results and write up findings.

Ethical Guardrails and Policy Alignment

• Legal compliance:
  • Abide by all applicable laws and institutional policies.
• Platform and model terms:
  • Respect provider terms for base models and data.
• Publication hygiene:
  • Do not disclose operational steps that enable misuse.
• Model stewardship:
  • Keep modified checkpoints private and access-controlled.

A responsible workflow recognizes both the research value and the dual-use risk—and errs on the side of caution.

Summary and Next Steps

Heretic automates the discovery and suppression of refusal directions in LLMs through directional ablation and orthogonal blocking. It applies a layer-wise kernel to minimize collateral damage to model quality, runs iterative trials to refine parameters, and outputs a modified state for controlled testing. Results vary by model, and runtime can range from minutes to hours.

Use cases include red teaming, alignment research, and behavior analysis in restricted settings. Do not deploy decensored models for general tasks, do not share modified checkpoints, and do not include operational details that would enable misuse. If you work in safety research, this approach offers a structured way to study refusal mechanisms and their tradeoffs—provided you set firm boundaries and document everything you do.