elder-plinius/OBLITERATUS

Health v2 · maintenance only

Adoption not part of the score

8056 stars · 1457 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

OBLITERATUS is a Python toolkit for abliteration — identifying and removing refusal behaviors from large language models by locating and intervening on refusal directions in hidden states, without retraining. It ships with a Gradio interface on HuggingFace Spaces, a Python API exposing intermediate artifacts, and crowd-sourced telemetry for abliteration research.

Use cases

• remove refusal behavior from an LLM without fine-tuning
• visualize where refusal directions live across model layers
• compare abliteration extraction methods like PCA and mean-difference
• benchmark an abliterated model against its baseline
• chat with an uncensored model side-by-side with the original
• study refusal mechanisms in transformer hidden states

When to choose

• you want to modify a model's refusal behavior without retraining or fine-tuning
• you need a no-code Gradio UI or Colab notebook for abliteration
• you're researching refusal directions and want access to activation tensors and direction vectors
• you want to contribute benchmark data to distributed abliteration research

When to avoid

• you need a model that enforces safety guardrails or content policies
• you want general fine-tuning rather than targeted behavior removal
• you lack GPU resources and can't use the hosted HuggingFace Space
• removing model gatekeeping is legally or ethically problematic in your deployment context

Facets

library · maturity active

machine-learning llm-inference llm-training data-visualization benchmarking large-language-models machine-learning artificial-intelligence developer-tools python cloud abliteration refusal-removal model-editing activation-steering interpretability gradio uncensored-models llm-safety-research gpu web-server

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markdown · JSON · MCP: product_card(name="elder-plinius/OBLITERATUS")