Which method allows customization without changing all model parameters?

The method that allows for customization without changing all model parameters is LoRa (Low-Rank Adaptation). This approach focuses on modifying only a small number of parameters through a low-rank matrix decomposition, which effectively reduces the number of parameters that need to be updated during the fine-tuning process. This is particularly advantageous in applications requiring efficiency, as it maintains model performance while reducing the computational burden and storage requirements, making it possible to implement changes tailored to specific tasks without the need for a complete retraining or adjustment of the entire model. This contrasts with other methods that may require full model modifications or do not provide the same level of efficiency in parameter adjustment.