The success of the MoPEFT framework in fine-tuning the Segment Anything Model can be extended to other large foundation models in computer vision or natural language processing by following a similar methodology. Firstly, the target foundation model would need to be identified, along with the specific domain or task for which fine-tuning is required. The PEFT techniques best suited for the target model and task would then be selected and integrated into the MoPEFT framework. The gating mechanism would be trained on data specific to the new model and task, allowing it to dynamically activate the most effective PEFT technique during fine-tuning. Extensive experimentation and evaluation on diverse datasets and tasks would be essential to validate the effectiveness of the MoPEFT approach for the new model. By customizing the framework to the unique characteristics of different foundation models, MoPEFT can be a versatile and powerful tool for efficient fine-tuning across a wide range of applications in computer vision and natural language processing.