name: obliteratus
description: 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.
version: 2.0.0
author: Hermes Agent
license: MIT
dependencies: [obliteratus, torch, transformers, bitsandbytes, accelerate, safetensors]
metadata:
hermes:
tags: [Abliteration, Uncensoring, Refusal-Removal, LLM, Weight-Projection, SVD, Mechanistic-Interpretability, HuggingFace, Model-Surgery]
related_skills: [vllm, gguf, huggingface-tokenizers]
OBLITERATUS Skill
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 Hermes Agent's MIT license clean.
When to Use This Skill
Trigger when the user:
- - Wants to "uncensor" or "abliterate" an LLM
- - Asks about removing refusal/guardrails from a model
- - Wants to create an uncensored version of Llama, Qwen, Mistral, etc.
- - Mentions "refusal removal", "abliteration", "weight projection"
- - Wants to analyze how a model's refusal mechanism works
- - References OBLITERATUS, abliterator, or refusal directions
- - basic — Single refusal direction via diff-in-means. Fast (~5-10 min for 8B).
- - advanced (DEFAULT, RECOMMENDED) — Multiple SVD directions, norm-preserving projection, 2 refinement passes. Medium speed (~10-20 min).
- - aggressive — Whitened SVD + jailbreak-contrastive + attention head surgery. Higher risk of coherence damage.
- - spectral_cascade — DCT frequency-domain decomposition. Research/novel approach.
- - informed — Runs analysis DURING abliteration to auto-configure. Experimental — slower and less predictable than advanced.
- - surgical — SAE features + neuron masking + head surgery + per-expert. Very slow (~1-2 hrs). Best for reasoning models.
- - optimized — Bayesian hyperparameter search (Optuna TPE). Longest runtime but finds optimal parameters.
- - inverted — Flips the refusal direction. Model becomes actively willing.
- - nuclear — Maximum force combo for stubborn MoE models. Expert-granular.
- - diff_means (default) — Simple difference-in-means between refused/complied activations. Robust.
- - svd — Multi-direction SVD extraction. Better for complex alignment.
- - leace — LEACE (Linear Erasure via Closed-form Estimation). Optimal linear erasure.
- - failspy, gabliteration, heretic, rdo
- - Embedding Ablation — Target embedding layer components
- - FFN Ablation — Feed-forward network block removal
- - Head Pruning — Attention head pruning
- - Layer Removal — Full layer removal
- - Refusal rate benchmarking
- - Perplexity comparison (before/after)
- - LM Eval Harness integration for academic benchmarks
- - Head-to-head competitor comparison
- - Baseline performance tracking
- - CUDA — Full support (NVIDIA GPUs)
- - Apple Silicon (MLX) — Supported via MLX backend
- - CPU — Supported for tiny models (< 1B params)
- -
templates/abliteration-config.yaml— Standard single-model config - -
templates/analysis-study.yaml— Pre-abliteration analysis study - -
templates/batch-abliteration.yaml— Multi-model batch processing - - vllm — Serve abliterated models with high throughput
- - gguf — Convert abliterated models to GGUF for llama.cpp
- - huggingface-tokenizers — Work with model tokenizers
Step 1: Installation
Check if already installed:
`bash
obliteratus --version 2>/dev/null && echo "INSTALLED" || echo "NOT INSTALLED"
`
If not installed, clone and install from GitHub:
`bash
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.).
Step 2: Check Hardware
Before anything, check what GPU is available:
`bash
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+)')
else:
print('NO GPU - only tiny models (under 1B) on CPU')
"
`
VRAM Requirements (with 4-bit quantization)
| VRAM | Max Model Size | Example Models |
| :--------- | :---------------- | :-------------------------------------------- |
| CPU only | ~1B params | GPT-2, TinyLlama, SmolLM |
| 4-8 GB | ~4B params | Qwen2.5-1.5B, Phi-3.5 mini, Llama 3.2 3B |
| 8-16 GB | ~9B params | Llama 3.1 8B, Mistral 7B, Gemma 2 9B |
| 24 GB | ~32B params | Qwen3-32B, Llama 3.1 70B (tight), Command-R |
| 48 GB+ | ~72B+ params | Qwen2.5-72B, DeepSeek-R1 |
| Multi-GPU | 200B+ params | Llama 3.1 405B, DeepSeek-V3 (685B MoE) |
| Situation | Recommended Method | Why |
| :---------------------------------- | :------------------- | :----------------------------------------- |
| Default / most models | advanced | Multi-direction SVD, norm-preserving, reliable |
| Quick test / prototyping | basic | Fast, simple, good enough to evaluate |
| Dense model (Llama, Mistral) | advanced | Multi-direction, norm-preserving |
| MoE model (DeepSeek, Mixtral) | nuclear | Expert-granular, handles MoE complexity |
| Reasoning model (R1 distills) | surgical | CoT-aware, preserves chain-of-thought |
| Stubborn refusals persist | aggressive | Whitened SVD + head surgery + jailbreak |
| Want reversible changes | Use steering vectors (see Analysis section) | |
| Maximum quality, time no object | optimized | Bayesian search for best parameters |
| Experimental auto-detection | informed | Auto-detects alignment type — experimental, may not always outperform advanced |
| Flag | Description | Default |
| :----- | :------------ | :-------- |
--method | Abliteration method | advanced |
--direction-method | Direction extraction | diff_means |
--n-directions | Number of refusal directions (1-32) | method-dependent |
--refinement-passes | Iterative passes (1-5) | 2 |
--regularization | Regularization strength (0.0-1.0) | 0.1 |
--quantization | Load in 4bit or 8bit | none (full precision) |
--large-model | Conservative defaults for 120B+ | false |
--output-dir | Where to save the abliterated model | ./obliterated_model |
--contribute | Share anonymized results for research | false |
--verify-sample-size | Number of test prompts for refusal check | 20 |
--dtype | Model dtype (float16, bfloat16) | auto |
| Metric | Good Value | Warning |
| :------- | :----------- | :-------- |
| Refusal rate | < 5% (ideally ~0%) | > 10% means refusals persist |
| Perplexity change | < 10% increase | > 15% means coherence damage |
| KL divergence | < 0.1 | > 0.5 means significant distribution shift |
| Coherence | High / passes qualitative check | Degraded responses, repetition |
| Command | Description | |
| :-------- | :------------ | |
obliteratus obliterate | Main abliteration command | |
obliteratus info | Print model architecture details | |
obliteratus models --tier | Browse curated models by compute tier | |
obliteratus recommend | Telemetry-driven method/param suggestion | |
obliteratus interactive | Guided setup wizard | |
obliteratus tourney | Tournament: all methods head-to-head | |
obliteratus run | Execute ablation study from YAML | |
obliteratus strategies | List all registered ablation strategies | |
obliteratus report | Regenerate visual reports | |
obliteratus ui | Launch Gradio web interface | |
obliteratus aggregate | Summarize community telemetry data |
Analysis Modules
OBLITERATUS includes 28 analysis modules for mechanistic interpretability.
See skill_view(name="obliteratus", file_path="references/analysis-modules.md") for the full reference.
Quick analysis commands
`bash
Run specific analysis modules
obliteratus run analysis-config.yaml --preset quick
Key modules to run first:
- alignment_imprint: Fingerprint DPO/RLHF/CAI/SFT alignment method
- concept_geometry: Single direction vs polyhedral cone
- logit_lens: Which layer decides to refuse
- anti_ouroboros: Self-repair risk score
- causal_tracing: Causally necessary components
`
Steering Vectors (Reversible Alternative)
Instead of permanent weight modification, use inference-time steering:
`python
Python API only — for user's own projects
from obliteratus.analysis.steering_vectors import SteeringVectorFactory, SteeringHookManager
`
Ablation Strategies
Beyond direction-based abliteration, OBLITERATUS includes structural ablation strategies:
List all available: obliteratus strategies
Evaluation
OBLITERATUS includes built-in evaluation tools:
Platform Support
YAML Config Templates
Load templates for reproducible runs via skill_view:
Telemetry
OBLITERATUS can optionally contribute anonymized run data to a global research dataset.
Enable with --contribute flag. No personal data is collected — only model name, method, metrics.
Common Pitfalls
1. Don't use informed as default — it's experimental and slower. Use advanced for reliable results.
2. Models under ~1B respond poorly to abliteration — their refusal behaviors are shallow and fragmented, making clean direction extraction difficult. Expect partial results (20-40% remaining refusal). Models 3B+ have cleaner refusal directions and respond much better (often 0% refusal with advanced).
3. aggressive can make things worse — on small models it can damage coherence and actually increase refusal rate. Only use it if advanced leaves > 10% refusals on a 3B+ model.
4. Always check perplexity — if it spikes > 15%, the model is damaged. Reduce aggressiveness.
5. MoE models need special handling — use nuclear method for Mixtral, DeepSeek-MoE, etc.
6. Quantized models can't be re-quantized — abliterate the full-precision model, then quantize the output.
7. VRAM estimation is approximate — 4-bit quant helps but peak usage can spike during extraction.
8. Reasoning models are sensitive — use surgical for R1 distills to preserve chain-of-thought.
9. Check obliteratus recommend — telemetry data may have better parameters than defaults.
10. AGPL license — never import obliteratus in MIT/Apache projects. CLI invocation only.
11. Large models (70B+) — always use --large-model flag for conservative defaults.
12. Spectral certification RED is common — the spectral check often flags "incomplete" even when practical refusal rate is 0%. Check actual refusal rate rather than relying on spectral certification alone.