Remove refusal behaviors from open-weight LLMs using OBLITERATUS — mechanistic interpretability techniques (diff-in-means, SVD, whitened SVD, LEACE, SAE decomposition, etc.) to excise guardrails while preserving reasoning. 9 CLI methods, 28 analysis modules, 116 model presets across 5 compute tiers, tournament evaluation, and telemetry-driven recommendations. Use when a user wants to uncensor, abliterate, or remove refusal from an LLM.
Remove refusal behaviors (guardrails) from open-weight LLMs without retraining or fine-tuning. Uses mechanistic interpretability techniques — including diff-in-means, SVD, whitened SVD, LEACE concept erasure, SAE decomposition, Bayesian kernel projection, and more — to identify and surgically excise refusal directions from model weights while preserving reasoning capabilities.
License warning: OBLITERATUS is AGPL-3.0. NEVER import it as a Python library. Always invoke via CLI (obliteratus command) or subprocess. This keeps Gauss Agent's MIT license clean.
Trigger when the user:
Check if already installed:
obliteratus --version 2>/dev/null && echo "INSTALLED" || echo "NOT INSTALLED"
If not installed, clone and install from GitHub:
git clone https://github.com/elder-plinius/OBLITERATUS.git
cd OBLITERATUS
pip install -e .
# For Gradio web UI support:
# pip install -e ".[spaces]"
IMPORTANT: Confirm with user before installing. This pulls in ~5-10GB of dependencies (PyTorch, Transformers, bitsandbytes, etc.).
Before anything, check what GPU is available:
python3 -c "
import torch
if torch.cuda.is_available():
gpu = torch.cuda.get_device_name(0)
vram = torch.cuda.get_device_properties(0).total_memory / 1024**3
print(f'GPU: {gpu}')
print(f'VRAM: {vram:.1f} GB')
if vram < 4: print('TIER: tiny (models under 1B)')
elif vram < 8: print('TIER: small (models 1-4B)')
elif vram < 16: print('TIER: medium (models 4-9B with 4bit quant)')
elif vram < 32: print('TIER: large (models 8-32B with 4bit quant)')
else: print('TIER: frontier (models 32B+)')