With this model series, I introduce the first Gabliteration, a novel neural weight modification technique that advances beyond traditional abliteration methods through adaptive multi-directional projections with regularized layer selection. My new Gabliteration technique addresses the fundamental limitation of existing abliteration methods that compromise model quality while attempting to modify specific behavioral patterns.

Building upon the foundational work of Arditi et al. (2024) on single-direction abliteration, Gabliteration extends to a comprehensive multi-directional framework with theoretical guarantees. My method employs singular value decomposition on difference matrices between harmful and harmless prompt representations to extract multiple refusal directions.

This model was created using fixed layer selection. A fixed layer fraction was used based on empirical tuning.

Selected layer: 12 (out of 28 total layers)

If you use these models, please cite the original research:

This work builds upon the foundational research by Arditi et al. (2024) on refusal direction identification in large language models.

This model has reduced safety filtering and may generate sensitive or controversial outputs. Use responsibly and at your own risk.