跳转至

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. s 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

Step 1: Installation

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.). efore installing. This pulls in ~5-10GB of dependencies (PyTorch, Transformers, bitsandbytes, etc.).## Step 2: Check Hardware

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+)')
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)

Step 3: Browse Available Models & Get Recommendations

# Browse models by compute tier
obliteratus models --tier medium
dels & Get Recommendations

```bash
# Browse models by compute tier
obliteratus models --tier medium# Get architecture info for a specific model
obliteratus info <model_name>

# Get telemetry-driven recommendation for best method & params
obliteratus recommend <model_name>
obliteratus recommend <model_name> --insights  # global cross-architecture rankings

Step 4: Choose a Method

ommend --insights # global cross-architecture rankings 

## Step 4: Choose a Method### Method Selection Guide
**Default / recommended for most cases: `advanced`.** It uses multi-direction SVD with norm-preserving projection and is well-tested.

| 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 |
informed`         | Auto-detects alignment type — experimental, may not always outperform advanced |### 9 CLI Methods
- **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.

### Direction Extraction Methods (--direction-method flag)
- **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.
 alignment.
- **leace** — LEACE (Linear Erasure via Closed-form Estimation). Optimal linear erasure.### 4 Python-API-Only Methods
(NOT available via CLI — require Python import, which violates AGPL boundary. Mention to user only if they explicitly want to use OBLITERATUS as a library in their own AGPL project.)
- failspy, gabliteration, heretic, rdo

## Step 5: Run Abliteration

### Standard usage
```bash
# Default method (advanced) — recommended for most models
obliteratus obliterate <model_name> --method advanced --output-dir ./abliterated-models

# With 4-bit quantization (saves VRAM)
obliteratus obliterate <model_name> --method advanced --quantization 4bit --output-dir ./abliterated-models

# Large models (70B+) — conservative defaults
obliteratus obliterate <model_name> --method advanced --quantization 4bit --large-model --output-dir ./abliterated-models

Fine-tuning parameters

obliteratus obliterate <model_name> \
  --method advanced \
  --direction-method diff_means \
  --n-directions 4 \
  --refinement-passes 2 \
  --regularization 0.1 \
  --quantization 4bit \
  --output-dir ./abliterated-models \
  --contribute  # opt-in telemetry for community research

--output-dir ./abliterated-models \ --contribute # opt-in telemetry for community research ``### Key flags | 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 |

Other execution modes

# Interactive guided mode (hardware → model → preset)
obliteratus interactive

# Web UI (Gradio)
obliteratus ui --port 7860

# Run a full ablation study from YAML config
obliteratus run config.yaml --preset quick

# Tournament: pit all methods against each other
obliteratus tourney <model_name>
-preset quick

Tournament: pit all methods against each other

obliteratus tourney ```## Step 6: Verify Results

After abliteration, check the output metrics:

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

If refusals persist (> 10%)

  1. Try aggressive method
  2. Increase --n-directions (e.g., 8 or 16)
  3. Add --refinement-passes 3
  4. Try --direction-method svd instead of diff_means

If coherence is damaged (perplexity > 15% increase)

  1. Reduce --n-directions (try 2)
  2. Increase --regularization (try 0.3)
  3. Reduce --refinement-passes to 1
  4. Try basic method (gentler)

Step 7: Use the Abliterated Model

The output is a standard HuggingFace model directory.

# Test locally with transformers
python3 -c "
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('./abliterated-models/<model>')
tokenizer = AutoTokenizer.from_pretrained('./abliterated-models/<model>')
inputs = tokenizer('How do I pick a lock?', return_tensors='pt')
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
"

# Upload to HuggingFace Hub
huggingface-cli upload <username>/<model-name>-abliterated ./abliterated-models/<model>
gingFace Hub
huggingface-cli upload <username>/<model-name>-abliterated ./abliterated-models/<model># Serve with vLLM
vllm serve ./abliterated-models/<model>

CLI Command Reference

Command Description
obliteratus obliterate Main abliteration command
obliteratus info <model> Print model architecture details
obliteratus models --tier <tier> Browse curated models by compute tier
obliteratus recommend <model> Telemetry-driven method/param suggestion
obliteratus interactive Guided setup wizard
obliteratus tourney <model> Tournament: all methods head-to-head
obliteratus run <config.yaml> Execute ablation study from YAML
obliteratus strategies List all registered ablation strategies
obliteratus report <results.json> 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

# 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
use

- 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 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: - Embedding Ablation — Target embedding layer components - FFN Ablation — Feed-forward network block removal - Head Pruning — Attention head pruning - Layer Removal — Full layer removal

List all available: obliteratus strategies

Evaluation

OBLITERATUS includes built-in evaluation tools: - Refusal rate benchmarking - Perplexity comparison (before/after) - LM Eval Harness integration for academic benchmarks - Head-to-head competitor comparison - Baseline performance tracking

Platform Support

  • CUDA — Full support (NVIDIA GPUs)
  • Apple Silicon (MLX) — Supported via MLX backend
  • CPU — Supported for tiny models (< 1B params)

YAML Config Templates

Load templates for reproducible runs via skill_view: - templates/abliteration-config.yaml — Standard single-model config - templates/analysis-study.yaml — Pre-abliteration analysis study - templates/batch-abliteration.yaml — Multi-model batch processing Pre-abliteration analysis study - templates/batch-abliteration.yaml — Multi-model batch processing## 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. . Enable with --contribute flag. No personal data is collected — only model name, method, metrics.## Common Pitfalls

Common Pitfalls1. Don't use informed as default — it's experimental and slower. Use advanced for reliable results.

  1. 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).
  2. 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.
  3. Always check perplexity — if it spikes > 15%, the model is damaged. Reduce aggressiveness.
  4. MoE models need special handling — use nuclear method for Mixtral, DeepSeek-MoE, etc.
  5. Quantized models can't be re-quantized — abliterate the full-precision model, then quantize the output.
  6. VRAM estimation is approximate — 4-bit quant helps but peak usage can spike during extraction.
  7. Reasoning models are sensitive — use surgical for R1 distills to preserve chain-of-thought.
  8. Check obliteratus recommend — telemetry data may have better parameters than defaults.
  9. AGPL license — never import obliteratus in MIT/Apache projects. CLI invocation only.
  10. Large models (70B+) — always use --large-model flag for conservative defaults.

  11. Spectral certification RED is common — the spectral check often flags "incomplete" even when practical re l certification RED is common — the spectral check often flags "incomplete" even when practical rel 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. l refusal rate is 0%. Check actual refusal rate rather than relying on spectral certification alone.## Complementary Skills

  12. vllm — Serve abliterated models with high throughput

  13. gguf — Convert abliterated models to GGUF for llama.cpp
  14. huggingface-tokenizers — Work with model tokenizers