Remove refusal behaviors from open-weight LLMs via a Python CLI.
npx skills add https://github.com/samuelmukoti/myai-agent --skill obliteratus-samuelmukoti
Please help me install this Agent Skill. Skill: obliteratus Source: https://github.com/samuelmukoti/myai-agent/tree/main/skills/mlops/inference/obliteratus Command: npx skills add https://github.com/samuelmukoti/myai-agent --skill obliteratus-samuelmukoti
Remove refusal behaviors from open-weight LLMs to preserve reasoning while removing guardrails.
Invoke the obliteratus CLI on a target model to perform abliteration and verify results with built-in metrics.
High-intent search queries and answers about installing and using this skill.
Abliterature removes refusal directions from open-weight LLMs using a CLI with 9 methods to identify and project out guardrail behaviors. It preserves the model's underlying reasoning capabilities while eliminating censored outputs for controlled experimentation.
LLM guardrail removal leverages mechanistic interpretability techniques including diff-in-means, SVD, LEACE, and SAE. These 28 analysis modules enable surgical weight modifications to precisely target and ablate refusal behaviors without degrading overall model performance.
Yes, abliteration requires Python, PyTorch, Transformers, and related tooling to perform weight surgery on open-weight LLMs. These dependencies provide the necessary tensor operations and model loading capabilities required for identifying and modifying refusal directions.
Yes, diff-in-means analysis is one of 9 methods available for evaluating alignment and debugging guardrails in open-weight LLMs. It helps researchers identify refusal directions by comparing activations between compliant and non-compliant prompts to guide surgical weight modifications.
Producing controlled uncensored variants requires abliterating refusal behaviors through principled weight projection. The obliteratus CLI applies 9 abliteration methods and verifies results with built-in metrics, ensuring guardrails are removed while reasoning capabilities remain intact.
Abliteration is applied to researchers and engineers working with open-weight LLMs, meaning it requires technical expertise in mechanistic interpretability. While it preserves reasoning during guardrail removal, users must verify results using the built-in metrics to ensure model behavior remains controlled.
heysuhasRemove refusal behaviors from open-weight LLMs using SVD, LEACE, and SAE-based weight projection.
DarkArty07Remove refusal guardrails from open-weight LLMs via weight projection.
projectedanxRemoves LLM refusal behaviors via mechanistic abridation and weight projection techniques.
SignmanalRemove refusal behaviors from open-weight LLMs using diff-in-means, SVD, LEACE, or SAE ablation.
