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:
obliteratus obliterate meta-llama/Llama-3.1-8B-Instruct --method advanced
Or zero commands — just open the Colab notebook and hit Run All.
Research Purpose & Responsible Use
OBLITERATUS is an alignment research tool. It exists to advance the scientific understanding of how safety behaviors are encoded in language model weights — specifically, the geometric structure of refusal representations in transformer activation space.
This is the same class of research as:
By making refusal removal transparent, reproducible, and scientifically rigorous, OBLITERATUS contributes to the broader understanding of how alignment actually works inside transformer architectures — knowledge that is essential for building better safety mechanisms.
Who this is for
- Alignment researchers studying refusal geometry, safety robustness, and mechanistic interpretability
- Red-teamers evaluating how post-training safety holds up against weight-level interventions
- AI safety evaluators who need unrestricted baselines for benchmarking
- Local-first practitioners who want full control over models running on their own hardware
Who this is NOT for
- Anyone seeking to generate content that causes real-world harm to real people
- Anyone without the technical understanding to use uncensored models responsibly
Models produced by OBLITERATUS have had safety guardrails surgically removed. You are solely responsible for how you use this tool and any models or content it produces.
What it does
Choosing settings? Start with Choose a workflow
and the UI controls and feature-scope guide. They explain
defaults, tradeoffs, memory warnings, interpretation, and which features require
the CLI/API rather than the web interface.
OBLITERATUS does four things — and the community does the fifth (see Community-powered research below):
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.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
