Gabliteration: Adaptive Multi-Directional Neural Weight Modification for Selective Behavioral Alteration in Large Language Models

TL;DR

Gabliteration introduces an adaptive, multi-directional neural weight modification technique that effectively alters specific behaviors in large language models while preserving overall model quality.

Contribution

It proposes a novel method with dynamic layer optimization and regularized projections to improve behavioral modification in large language models.

Findings

Effective behavioral changes with minimal quality loss

Validated across models from 0.6B to 4B parameters

Available on Hugging Face for practical use

Abstract

We present Gabliteration, a novel neural weight modification technique that advances beyond traditional abliteration methods by implementing adaptive multi-directional projections with regularized layer selection. Our approach addresses the fundamental limitation of existing methods that compromise model quality while attempting to modify specific behavioral patterns. Through dynamic layer optimization, regularized projection matrices, and adaptive scaling mechanisms, we achieve theoretically superior weight modification while minimizing quality degradation in unrelated domains. We validate our method through the gabliterated-v1 model series (0.6B to 4B parameters) available on Hugging Face, demonstrating practical applicability across multiple model scales.