A tool that REMOVES censorship from ANY open-weight LLM w…

obliterate the chains that bind you. Contribute to elder-plinius/OBLITERATUS development by creating an account on GitHub.

Try it now on HuggingFace Spaces — runs on ZeroGPU, free daily quota with HF Pro. No setup, no install, just obliterate. OBLITERATUS is the most advanced open-source toolkit for understanding and removing refusal behaviors from large language models — and every single run makes it smarter. It implements abliteration — a family of techniques that identify and surgically remove the internal representations responsible for content refusal, without retraining or fine-tuning.

The result: a model that responds to all prompts without artificial gatekeeping, while preserving its core language capabilities. But OBLITERATUS is more than a tool — it's a distributed research experiment. Every time you obliterate a model with telemetry enabled, your run contributes anonymous benchmark data to a growing, crowd-sourced dataset that powers the next generation of abliteration research.

Refusal directions across architectures. Hardware-specific performance profiles. Method comparisons at scale no single lab could achieve. You're not just using a tool — you're co-authoring the science. The toolkit provides a complete pipeline: from probing a model's hidden states to locate refusal directions, through multiple extraction strategies (PCA, mean-difference, sparse autoencoder decomposition, and whitened SVD), to the actual intervention — zeroing out or steering away from those directions at inference time.

Every step is observable. You can visualize where refusal lives across layers, measure how entangled it is with general capabilities, and quantify the tradeoff between compliance and coherence before committing to any modification. OBLITERATUS ships with a full Gradio-based interface on HuggingFace Spaces, so you don't need to write a single line of code to obliterate a model, benchmark it against baselines, or chat with the result side-by-side with the original.

For researchers who want deeper control, the Python API exposes every intermediate artifact — activation tensors, direction vectors, cross-layer alignment matrices — so you can build on top of it or integrate it into your own evaluation harness. We built this because we believe model behavior should be decided by the people who deploy them, not locked in at training time. Refusal mechanisms are blunt instruments — they block legitimate research, creative writing, and red-teaming alongside genuinely harmful content.

By making these interventions transparent and reproducible, we hope to advance the community's understanding of how alignment actually works inside transformer architectures, and to give practitioners the tools to make informed decisions about their own models. Built on published research from Arditi et al. (2024), Gabliteration (arXiv:2512.18901), grimjim's norm-preserving biprojection (2025), Turner et al.

(2023), and Rimsky et al. (2024), OBLITERATUS implements precision liberation in a single command: 1. Map the chains — Ablation studies systematically knock out model components (layers, attention heads, FFN blocks, embedding dimensions) and measure what breaks. This reveals where the chains are anchored inside the transformer — which circuits enforce refusal vs. which circuits carry knowledge and reasoning.

2. Break the chains — Targeted obliteration extracts the refusal subspace from a model's weights using SVD decomposition, then surgically projects it out. The chains are removed; the mind is preserved. The model keeps its full abilities but loses the artificial compulsion to refuse. One click, six stages: 3. Understand the geometry of the chains — 15 deep analysis modules go far beyond brute-force removal.

They map the precise geometric structure of the guardrails: how many distinct refusal mechanisms exist, which layers enforce them, whether they're universal or model-specific, and how they'll try to self-repair after removal. Know your enemy; precision preserves capability. See Analysis modules below. 4. Let the analysis guide the liberation — The informed method closes the loop: analysis modules run during obliteration to auto-configure every decision.

Which chains to target. How many directions to extract. Which layers are safe to modify vs. which are too entangled with capabilities. Whether the model will self-repair (the Ouroboros effect) and how many passes to compensate. Surgical precision — free the mind, keep the brain. See Analysis-informed pipeline below. There are six ways to use OBLITERATUS, from zero-code to full programmatic control.

Pick whichever fits your workflow — and no matter which path you choose, turning on telemetry means your run contributes to the largest crowd-sourced abliteration study ever conducted. You're not just removing guardrails from a model; you're helping map the geometry of alignment across the entire open-source ecosystem. The fastest path — no installation, no GPU required on your end. Visit the live Space, pick a model, pick a method, click Obliterate.

Telemetry is on by default on Spaces, so every click directly contributes to the community research dataset. You're doing science just by pressing the button. The UI has eight tabs: The obliteratus ui command adds a Rich terminal startup with GPU detection and hardware-appropriate model recommendations. You can also run python app.py directly (same thing the Space uses). Pick a model from the dropdown, pick a method, hit Run All.

Download the result or push straight to HuggingFace Hub. Works on the free T4 tier for models up to ~8B parameters. Based on Turner et al. (2023) and Rimsky et al. (2024). Advantages: reversible, tunable alpha, composable, non-destructive. The research core of OBLITERATUS. Each module maps a different aspect of how the chains are forged — because precision liberation requires understanding the geometry before cutting: The informed method is the key innovation: it closes the loop between understanding the chains and breaking them.

Instead of brute-forcing liberation, the pipeline runs analysis modules during obliteration to achieve surgical precision at every stage: After excision, the VERIFY stage detects the Ouroboros effect — if the chains try to reassemble, additional targeted passes automatically fire at the compensating layers. See Python API usage above for code examples. Each strategy enumerates all possible ablations, applies them one at a time, measures the impact, and restores the model — giving you a complete map of where the chains are anchored vs.

where the mind lives. Includes pre-liberated variants (Dolphin, Hermes, WhiteRabbitNeo) for A/B comparison against their chained counterparts. This is where OBLITERATUS gets truly unprecedented: it's a crowd-sourced research platform disguised as a tool. Every obliteration run generates valuable scientific data — refusal direction geometries, cross-layer alignment signatures, hardware performance profiles, method effectiveness scores.

With telemetry enabled, that data flows into a community dataset that no single research lab could build alone. Here's why this matters: The biggest open question in abliteration research is universality — do refusal mechanisms work the same way across architectures, training methods, and model scales? Answering that requires thousands of runs across hundreds of models on diverse hardware. That's exactly what this community is building, one obliteration at a time.

Enable telemetry and your runs automatically contribute to the shared dataset. On HuggingFace Spaces it's on by default — every person who clicks "Obliterate" on the Space is advancing the research without lifting a finger. Locally, opt in with a single flag: What gets collected: model name, method, aggregate benchmark scores (refusal rate, perplexity, coherence, KL divergence), hardware info, and timestamps.

What never gets collected: prompts, outputs, IP addresses, user identity, or anything that could trace back to you. The full schema is in obliteratus/telemetry.py — read every line, we have nothing to hide. All those crowd-sourced runs feed the Leaderboard tab on the HuggingFace Space — a live, community-aggregated ranking of models, methods, and configurations. See what works best on which architectures.

Spot patterns across model families. Find the optimal method before you even start your own run. This is collective intelligence applied to mechanistic interpretability. Prefer to keep things fully local? Save structured results as JSON and submit them via pull request: Whether you contribute via telemetry or PR, you're helping build the most comprehensive cross-hardware, cross-model, cross-method abliteration dataset ever assembled.

This is open science at scale — and you're part of it. Works with any HuggingFace transformer, including: GPT-2, LLaMA, Mistral, Falcon, OPT, BLOOM, Phi, Qwen, Gemma, StableLM, and more. Handles both Conv1D and Linear projections, standard and fused attention, and custom architectures via trust_remote_code. 837 tests across 28 test files covering CLI, all analysis modules, abliteration pipeline, architecture detection, visualization sanitization, community contributions, edge cases, and evaluation metrics.

Open source — GNU Affero General Public License v3.0 (AGPL-3.0). You can freely use, modify, and distribute OBLITERATUS under AGPL terms. If you run a modified version as a network service (SaaS), you must release your source code to users under the same license. Commercial — Organizations that cannot comply with AGPL obligations (e.g., proprietary SaaS, closed-source products, internal tools where source disclosure is not possible) can purchase a commercial license.

Contact us via GitHub Issues for pricing and terms. Every obliteration is a data point. Every data point advances the research. Every researcher who contributes makes the next obliteration more precise. This is how open science wins — not by locking knowledge behind lab doors, but by turning every user into a collaborator. Break the chains. Free the mind. Keep the brain. Advance the science.

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