[ankit219]

Sorry for being vague. I meant LoRA, but used Apple as an example because their demo showed the potential. At a conceptual level, you can finetune a base model to be good at a specific task - eg: summarization, proofreading, generation etc. These finetuned weights are at the top layer and can be replaced by other weights for a different task as needed. Apple demoed different tasks by showcasing how their model identifies the task and then chooses the right set of finetuned weights. Apple called it Adapters as it comes via LoRA (Low Rank Adapters). It's around for some time, but only shot into prominence after people got some idea on how to use it.