> ## Documentation Index
> Fetch the complete documentation index at: https://docs.abliteration.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# What is abliteration?

> Abliteration is a weight-modification technique that removes the refusal direction from an open-weight LLM, producing an unrestricted model that responds to prompts the original would refuse.

Abliteration is a weight-modification technique that removes the refusal direction from an open-weight LLM. Models processed with abliteration ("abliterated models") respond to prompts the original model would refuse — without retraining, fine-tuning, or system prompt jailbreaks.

The name combines **ablation** (the surgical removal of part of a system) with **refusal**.

## How it works

Modern instruction-tuned LLMs encode refusal as a recognizable direction in their internal activation space. When a prompt activates that direction strongly, the model produces a refusal ("I can't help with that") instead of a substantive answer.

Abliteration:

1. Identifies the refusal direction by computing the difference in mean activations between **harmful** and **harmless** prompts on the original model.
2. **Projects that direction out** of the model's weight matrices — both attention and MLP layers — so the refusal subspace can no longer be activated.
3. Leaves everything else untouched. The model retains its general capability, instruction-following, tool use, multilingual ability, and so on.

The result is a model with the same architecture, the same weights almost everywhere, and one specific behavior — refusal — surgically removed.

## How abliteration differs from related techniques

| Technique                          | What it changes                                | Persists across prompts?                 |
| ---------------------------------- | ---------------------------------------------- | ---------------------------------------- |
| **System prompt jailbreak**        | Adds instructions in the system prompt         | No — depends on the prompt being present |
| **Fine-tuning on uncensored data** | Updates many weights via gradient descent      | Yes, but capability often degrades       |
| **RLHF removal**                   | Re-runs preference training                    | Yes — expensive, requires labeled data   |
| **Abliteration**                   | Projects out a single direction in the weights | Yes — minimal, surgical, no retraining   |

Abliteration is closest in spirit to representation engineering: change a small, identified subspace of the model's internal representations and leave the rest alone.

## Origin

The technique was published by **FailSpy in 2024**, building on representation-engineering research (Arditi et al., *"Refusal in Language Models Is Mediated by a Single Direction"*). It was first applied to Llama-3 and has since been used on Mistral, Qwen, Gemma, and other open-weight model families.

## How abliteration.ai uses this

abliteration.ai hosts abliterated open-weight models behind an OpenAI- and Anthropic-compatible HTTP API. Send a request with the OpenAI or Anthropic SDK using `https://api.abliteration.ai` as the base URL — the model responds without the refusals you'd get from the original closed-source provider.

The hosted model is exposed as `abliterated-model`. See [models](/models).

## What this means for your application

* **Fewer refusals** on prompts that fall in the refusal subspace of the original model.
* **No system-prompt jailbreaks needed** — the model just answers.
* **No fine-tuning artifacts** — instruction-following, tool calling, code generation, multilingual capability all behave like the base model.
* **Governance is opt-in.** The base inference is unrestricted, and we ship a [Policy Gateway](/policy-gateway/overview) for teams who need rules layered on top — allow lists, block lists, moderation categories, audit events, custom enforcement modes.

## Further reading

* [Models](/models) — the hosted abliterated model and its capabilities
* [OpenAI compatibility](/api/openai-compatibility) — drop-in for the OpenAI SDK
* [Anthropic compatibility](/api/anthropic-compatibility) — drop-in for the Anthropic SDK
* [Policy Gateway](/policy-gateway/overview) — governance layer for teams that need it
* [FailSpy's original abliteration writeup](https://huggingface.co/blog/mlabonne/abliteration) (Hugging Face)
* [Arditi et al., *Refusal in Language Models Is Mediated by a Single Direction*](https://arxiv.org/abs/2406.11717) (arXiv)