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Ablate: Directional Ablation Toolkit for Open LLMs

Directional ablation of open LLMsKL-guided Optuna searchLLM-judge evaluation harnessPresented at AI Tinkerers Dubai

In short

Ablate is a research toolkit from AI Anytime for mechanistic interpretability and AI safety. It finds the linear direction in a transformer's residual stream that mediates a behaviour — by default refusal — and removes it at runtime or permanently, using a KL-divergence-guided search to balance the change against capability damage. It was demoed live at AI Tinkerers Dubai in October 2026.

Find the direction

Ablate locates the residual-stream direction that mediates a behaviour, using short trigger prompts that exist only to find it.

Remove it, carefully

Ablate at runtime with forward hooks, or permanently by orthogonalising weights; an automatic search tunes which direction, how strongly and which layers.

Measure the cost

An LLM-judge harness and KL divergence quantify both the behaviour change and the damage to the model's other capabilities.

Built for small models

GPT-2, SmolLM2, TinyLlama and Qwen2.5 0.5B–1.5B — models you can study on a laptop or a free Colab GPU.

Key facts

Vertical
AI Anytime Community
For
Researchers and engineers studying how safety behaviour is represented in small open models
Price
Source available on GitHub · pip install ablate-llm

FAQs

Ablate: FAQs

More answers on the FAQ page, or ask us.

What is Ablate?

Ablate is AI Anytime's toolkit for directional ablation (abliteration): it finds the residual-stream direction that mediates refusal in small open language models and removes it, for interpretability and safety research.

Where was Ablate presented?

Ablate was demoed live at AI Tinkerers Dubai in October 2026.

Is Ablate open source?

The source is publicly available on GitHub for viewing and evaluation, and the package installs with pip install ablate-llm. Other uses require written permission under its licence.

Is Ablate meant for misuse?

No. It is dual-use research tooling for studying how safety behaviour is represented and how robust it is. Use it only on models and in contexts you are authorised to.

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