Modern AI language models are extremely capable, but adapting them to new tasks can be resource-heavy — requiring lots of memory, computing power, and to change the many internal parameters. To make this easier, researchers have developed techniques that aim to update only a small number of these parameters, making the fine-tuning process more efficient.One popular method, called LoRA (Low-Rank Adaptation), strikes a strong balance: it keeps the number of new parameters low and remains efficient in terms of memory, speed, and performance. However, many newer methods reduce the number of added parameters even further — but at the cost of using more memory, more computation, or losing accuracy.We created WeGeFT (short for Weight-Generative Fine-Tuning, and pronounced as wee-gift), a new approach that keeps LoRA’s broad efficiency benefits while reducing the number of added parameters even more. It learns how to generate the necessary updates directly from the original model’s knowledge, using a simple and compact design. Despite being lightweight, WeGeFT matches or outperforms LoRA on a wide range of tasks — from arithmetic and commonsense reasoning, following instructions to coding and image recognition — making it a powerful and efficient tool for tuning AI models.