Parameter-Efficient Fine-Tuning (PEFT): Fine-tune Large Language Models with Limited Resources
Comperhishive Introduction to Parameter-Efficient Fine-Tuning (PEFT)
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, provide a cost-effective approach for refining substantial language models, utilizing only a fraction of their parameters.
This obviates the need for resource-intensive complete fine-tuning and facilitates training with constrained computational resources. PEFT’s modular design makes it adaptable for various tasks, while techniques like 4-bit precision quantization further decrease memory requirements.
In summary, PEFT expands the accessibility of powerful large language models to a broader user base.
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