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.

翻译：本文提出Gabliteration，一种创新的神经权重修改技术。该方法通过实现带正则化层选择的自适应多向投影，超越了传统的权重消除方法。现有方法在尝试修改特定行为模式时往往损害模型质量，我们的技术从根本上解决了这一局限。通过动态层优化、正则化投影矩阵和自适应缩放机制，我们在实现理论更优的权重修改的同时，最大程度减少了无关领域的质量损失。我们在Hugging Face平台发布的gabliterated-v1模型系列（0.6B至4B参数）上验证了本方法，证明了其在多种模型规模上的实际适用性。