A Paradigm Shift: MoRA’s Role in Advancing Parameter-Efficient Fine-Tuning Techniques

Parameter-efficient fine-tuning (PEFT) techniques adapt large language models (LLMs) to specific tasks by modifying a small subset of parameters, unlike Full Fine-Tuning (FFT), which updates all parameters. PEFT, exemplified by Low-Rank Adaptation (LoRA), significantly reduces memory requirements by updating less than 1% of parameters while achieving similar performance to FFT. LoRA uses low-rank matrices to […]

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