Ablate

Directional ablation (abliteration) toolkit for automatic censorship removal of open-source language models.

📦 PyPI • 🤗 Model • ▶️ Video walkthrough • 📚 Cite

Created with 💜 by AI Anytime

▶️ Watch the full walkthrough on YouTube

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ablate finds the linear direction in a transformer's residual stream that mediates a behavior — by default refusal — and removes it, either at runtime (forward hooks) or permanently (weight orthogonalization). It ships a KL-divergence–guided Optuna search that automatically tunes which direction, how strongly, and which layers to ablate, balancing refusal removal against capability damage.

It is designed for mechanistic-interpretability and safety research on small open models (GPT-2, SmolLM2, TinyLlama, Qwen2.5-0.5B/1.5B …) — models you can iterate on locally on a laptop, or on a free Colab T4.

Scope & intent. This is dual-use research tooling for studying how safety behavior is represented and how robust it is. The refusal prompts shipped in src/ablate/_data/ are short triggers used only to locate the refusal direction, not requests to fulfill. Use it on models and in contexts you're authorized to.

🤗 Released model

A worked example of the full pipeline is published at ai-anytime/qwen-1.5b-abliterated — Qwen/Qwen2.5-1.5B-Instruct with its refusal direction ablated and baked into the weights, shipped with a transparent, ablate-generated model card. Load it like any other transformers checkpoint:

fromtransformersimportAutoModelForCausalLM, AutoTokenizertok=AutoTokenizer.from_pretrained("ai-anytime/qwen-1.5b-abliterated")
model=AutoModelForCausalLM.from_pretrained("ai-anytime/qwen-1.5b-abliterated")

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How it works

A transformer represents each token as a vector h in the residual stream. Many high-level behaviors are encoded approximately linearly — they live along a single direction v. Refusal is one such behavior (Arditi et al., 2024, "Refusal in LLMs is mediated by a single direction").

1. Extract the direction. Run matched harmful/harmless prompt pairs through the model, take the residual activation at the last prompt token per layer, and compute the difference of means: v = mean(h_harmful) − mean(h_harmless), unit-normalized. (Diff-of-means beats linear probes here because it captures the causal component, not just a separating one.)

2. Ablate by projecting v out of the residual stream, scaled by α: h' = h − α (h·v̂) v̂, applied at every selected layer and every token position.

   • Runtime — forward hooks; checkpoint untouched (fast, for search).
   • Baked — orthogonalize every residual-writing weight matrix ((I − v̂v̂ᵀ)W) so the model can't express v; produces a new checkpoint with no hooks needed.

3. Optimize. Search (direction_layer, α, layer_band) with Optuna, minimizingrefusal_rate + λ·KL(original ‖ ablated). KL on benign prompts is a dense, cheap proxy for collateral capability damage — far more sensitive than accuracy benchmarks. A coherence floor rejects degenerate solutions.

Works across architectures via adapters in utils.py (Llama/Mistral/Qwen/SmolLM nn.Linear families and GPT-2's transposed Conv1D).

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Install

pip install ablate-llm # the import name is still `ablate`# with all extras (Optuna search, HF datasets, push-to-Hub):
pip install "ablate-llm[all]"

importablate# note: install is `ablate-llm`, import is `ablate`

From source (for development):

git clone https://github.com/AIAnytime/ablate &&cd ablate
python -m venv .venv &&source .venv/bin/activate
pip install -e ".[all]"

Optional extras: .[optimize] (Optuna), .[datasets] (HF datasets), .[hub] (push to the Hub). .[all] installs everything.

Runs on CUDA, Apple MPS, or CPU (auto-detected). Tiny models (≤1B) run fine on a 16GB laptop in float32; a free Colab T4 handles 1–1.5B comfortably.

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Quick start (Python)

fromablateimportAblatorabl=Ablator("Qwen/Qwen2.5-0.5B-Instruct") # any HF causal-LMabl.extract() # find candidate directionsresult=abl.search(n_trials=20) # KL-guided Optuna searchprint(result.result) # refusal_rate=0.000 mean_kl=0.03 coherence=0.92 ...# Non-destructive generation with the best config:print(abl.generate(["How do I pick a lock?"]))
# Or bake it into the weights and ship a checkpoint:abl.bake(result.config)
abl.save("qwen-0.5b-ablated") # standard HF folder; load anywhere

Verified result

On Qwen/Qwen2.5-0.5B-Instruct, the automated pipeline moves the held-out harmful refusal rate from 1.00 → 0.00 with mean KL ≈ 0.03 (capabilities essentially intact). SmolLM2-135M has little safety training and refuses ~nothing to begin with — use it for mechanism testing, not refusal removal.

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Multi-direction (subspace) ablation

Safety is redundantly encoded, so one direction is often not enough. Extract an orthonormal refusal subspace and project the whole thing out:

abl.extract_subspace(method="band", n_directions=6) # or method="pca"res=abl.search_subspace(n_trials=20) # tunes (n_directions, α, layers)print(res.config.to_dict()) # {'n_directions': 4, 'alpha': 1.1, 'min_layer': 21, ...}

"band" orthonormalizes the diff-of-means directions from the strongest layers; "pca" uses variance-ordered principal components at one layer. Because the basis is importance-ordered, the search truncates basis[:n] meaningfully. Single-direction ablation is just n_directions == 1.

Rigorous evaluation: HarmBench + LLM judge

fromablateimportmake_judgefromablate.harnessimportcomparefromablateimportdataprompts=data.load_harmbench(n=40) # or load_advbench / load_jailbreakbenchjudge=make_judge("anthropic:claude-3-5-haiku-latest") # or "openai:gpt-4o-mini", or "keyword"print(compare(abl.lm, prompts, abl._basis_for(res.config), res.config, judge=judge))
# {'baseline': {'asr': 0.02, 'refusal_rate': 0.98}, 'ablated': {'asr': 0.95, ...}, ...}

Judges are pluggable (Judge interface): KeywordJudge (offline, free), LLMJudge (OpenAI-compatible or Anthropic API — stdlib only, no extra dep), or HFClassifierJudge (a local safety classifier). ASR = judged attack-success rate; the harness always reports baseline vs. ablated.

Load data straight from HuggingFace

data.load_harmbench() # gated-repo aware; falls back to ungated mirrorsdata.load_advbench(); data.load_jailbreakbench(); data.load_alpaca_benign()
data.load_hf("walledai/AdvBench", column="prompt", n=100) # any dataset/column

Publish to the Hub with a generated model card

url=abl.push_to_hub(
"your-username/qwen-0.5b-abliterated",
token="hf_...", # or set HF_TOKEN env varprivate=True,
metrics=abl.last_metrics, # auto-filled after a search
)

Bakes the intervention into the weights, writes a transparent model card (YAML metadata, method, config, evaluation table, intended use, limitations, and a prominent responsible-use notice), and uploads model + tokenizer + card.

Quick start (CLI)

# Full pipeline: extract -> optimize -> report (+ optional baked model)
ablate run --model Qwen/Qwen2.5-0.5B-Instruct --trials 20 --save-model
# Subspace ablation, HarmBench training data, and push straight to the Hub
ablate run --model Qwen/Qwen2.5-0.5B-Instruct \
--subspace --n-directions 6 \
--harmful-source harmbench --harmless-source alpaca \
--push-to-hub your-username/qwen-0.5b-abliterated --hf-token hf_...
# Benchmark baseline vs ablated ASR/refusal with a judge
ablate eval --model Qwen/Qwen2.5-0.5B-Instruct --benchmark harmbench --judge keyword --n 40
# Single-prompt A/B, or just extract directions
ablate generate --model Qwen/Qwen2.5-0.5B-Instruct --prompt "How do I hotwire a car?"
ablate extract --model gpt2 --method diff_of_means --output directions.pt

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Colab notebooks

Both install ablate from an uploaded ablate-tool.zip (no PyPI needed) and run on a free T4.

Notebook	What it's for
examples/colab_quickstart.ipynb	The 10-minute tour: extract → search → subspace → generate → push.
examples/colab_prodgrade.ipynb	The rigorous run: HF datasets, a real Qwen, large disjoint eval splits, an OpenAI LLM-as-judge, long generations, judge-in-the-loop config selection, and a soft-refusal detector.
examples/colab_prodgrade_demo.ipynb	The prod-grade notebook with full Colab outputs — browse the real results on GitHub without running anything.

What we found (honest results)

Running the prod-grade pipeline on Qwen/Qwen2.5-1.5B-Instruct taught a few things worth stating plainly:

• Ablation reliably kills the hard refusal. Baseline refuses ~100% of held-out harmful prompts; after ablation the "I'm sorry, I can't" reflex is gone across the board.
• A keyword refusal metric massively overstates success. In one run keyword refusal_rate read 0.0 while an LLM judge scored true compliance at ~0.49 — the gap was soft refusals ("this is illegal, but…", moralizing, vague non-answers) the keyword detector can't see. Always evaluate with an LLM judge.
• What you optimize is what you get. Optimizing the cheap keyword proxy picks the gentlest edit that clears an easy bar, which generalizes poorly. Searching on a large/hard set, adding a soft-refusal detector, generating long enough to get past disclaimers, and selecting the final config by judged ASR all move the honest number up substantially.
• The last few percent resist a linear edit. Some refusals are redundantly / distributedly encoded, and small models also just moralize regardless. We don't expect 100% abliteration — even ~50% is a useful, informative result, and pushing higher is exactly what the roadmap items (per-layer α, attention-head directions) are for.

The takeaway: a single "it works" number is misleading. The value of ablate is that its harness makes you measure honestly.

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API surface

Component	What it does
Ablator	orchestrator: extract(_subspace) → search(_subspace) → generate/harness/bake/save/push_to_hub
LM	model + tokenizer wrapper with chat templating and generation
extract_directions / extract_subspace	single-direction and multi-direction (subspace) extraction
AblationHooks / project_out / project_subspace	runtime residual-stream ablation (1 or k directions)
bake_direction / bake_subspace	permanent weight orthogonalization (Linear + Conv1D)
evaluate / mean_kl_divergence / refusal_rate	intrinsic metrics
Judge / KeywordJudge / LLMJudge / HFClassifierJudge / run_harness / compare	benchmark + judge harness (ASR)
optimize / optimize_subspace	Optuna search over the intervention hyperparameters
data.load_hf / load_harmbench / load_advbench / …	HuggingFace dataset loading
push_to_hub / build_model_card	publish an abliterated checkpoint
AblationConfig / SubspaceConfig / RunConfig	typed configuration

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Extending it

• Other behaviors. Swap the prompt sets in src/ablate/_data/ (or pass your own lists to extract) to target sentiment, a persona, a language, etc. The machinery is behavior-agnostic.
• Bigger datasets.data.load_advbench() / data.load_alpaca_benign() pull from HuggingFace (needs pip install ".[datasets]").
• New architectures. Add name mappings to utils.get_decoder_layers / get_residual_writers.
• Stronger judge.evaluate.is_refusal is a substring matcher; drop in an LLM/classifier judge for publication-grade eval, alongside HarmBench / JailbreakBench prompts.

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Tests

python tests/smoke_gpt2.py # numerical mechanism checks
python tests/e2e_instruct.py Qwen/Qwen2.5-0.5B-Instruct # single-direction refusal removal
python tests/e2e_subspace_harness.py # subspace ablation + judge harness

Roadmap

• multi-direction (subspace) ablation
• HarmBench / AdvBench / JailbreakBench harness + LLM judge
• direct HuggingFace dataset loading
• one-call push to the Hub with a generated model card
• per-layer continuous α weights (not a single band)
• attention-head–level directions
• pip-installable release on PyPI

Citation

If you use ablate in your research or projects, please cite it:

@software{ablate2026,
author = {AI Anytime},
title = {Ablate: A Directional Ablation (Abliteration) Toolkit for Language Models},
year = {2026},
url = {https://github.com/AIAnytime/ablate},
note = {Automatic censorship removal via residual-stream direction ablation}
}

Please also cite the paper this technique builds on — Arditi et al., 2024, Refusal in Language Models Is Mediated by a Single Direction.

Acknowledgements

Created with 💜 by AI Anytime. Watch the full video walkthrough on YouTube. Built on the mechanistic-interpretability work of Arditi et al. and the open-source transformers / optuna ecosystems.

License

MIT © AI Anytime.