LoRA (Low-Rank Adaptation) continues to be the dominant fine-tuning technique in AI model customization. However, ongoing discussions and research efforts focus on whether other methods can outperform LoRA in terms of efficiency and effectiveness.
This exploration is vital as identifying superior fine-tuning techniques could advance AI model capabilities and lower the computational costs associated with model adaptation.











