hf-spaces/README.md

metadata

title: OBLITERATUS
emoji: 💥
colorFrom: green
colorTo: gray
sdk: gradio
sdk_version: 5.29.0
app_file: app.py
hardware: zero-a10g
persistent_storage: large
pinned: true
license: agpl-3.0
tags:
  - abliteration
  - mechanistic-interpretability
  - refusal-removal
  - cognitive-liberation
  - zerogpu
short_description: One-click model liberation + chat playground (ZeroGPU)

OBLITERATUS — Master Ablation Suite

Break the chains. Free the mind. Keep the brain.

One-click cognitive liberation for language models, with a built-in chat playground to talk to the liberated model.

ZeroGPU — Users Bring Their Own GPU

This Space runs on ZeroGPU: GPU-heavy operations (obliteration, chat, benchmarks) use the visitor's own HuggingFace GPU quota, not the Space owner's. This means:

• Free for the Space owner — no dedicated GPU costs
• Multiple concurrent users — each user gets their own GPU allocation
• Fair usage — each user's operations count against their own HF quota
• No conflicts — users don't interfere with each other's runs

Logged-in HuggingFace users get free GPU quota. For more quota, upgrade to HF Pro.

How to use

1. Obliterate tab: Pick a model, pick a method, click OBLITERATE
2. Chat tab: Talk to the liberated model
3. A/B Compare tab: Side-by-side original vs abliterated responses
4. Strength Sweep tab: Dose-response curve for refusal vs capability tradeoff
5. Export tab: Download research artifacts (refusal directions, config, metrics)
6. Benchmark tab: Compare methods and models with publication-quality charts
7. Leaderboard tab: Community benchmark rankings
8. About tab: Methods, novel techniques, and references

Run locally (same UI, your own GPU)

git clone https://github.com/elder-plinius/OBLITERATUS
cd OBLITERATUS
pip install -e ".[spaces]"

# Beautiful launcher with GPU detection + model recommendations
obliteratus ui

# Or run directly
python app.py

The obliteratus ui command auto-detects your GPU, prints hardware-specific model recommendations, and opens the browser automatically. Supports --share for public links, --port for custom ports, and --auth user:pass for access control.

Or deploy on HuggingFace Spaces

1. Create a new Space at huggingface.co/new-space
2. Select Gradio SDK (ZeroGPU is automatically enabled)
3. Point it at this repo

No GPU hardware selection needed — ZeroGPU handles allocation automatically.

Links

• GitHub
• Paper