Learn what PEFT methods are and how they can help you fine-tune large language models (LLMs) with minimal memory and maximal performance.
“PEFT methods can reduce the memory needed for fine-tuning dramatically, sometimes to just 12-20% of the memory needed for full fine-tuning.” Is this true or false?
The correct answer is A. True. PEFT methods can reduce the memory needed for fine-tuning dramatically, sometimes to just 12-20% of the memory needed for full fine-tuning. PEFT stands for Parameter-Efficient Fine-Tuning, and it is a class of techniques that enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model’s parameters. PEFT methods only fine-tune a small number of (extra) model parameters, such as adapters, prefixes, or soft prompts, that are inserted into the original model layers. This reduces the computational and storage costs of fine-tuning, as well as the risk of overfitting or catastrophic forgetting. According to a recent empirical analysis, PEFT methods can achieve comparable performance to full fine-tuning while using much less memory and time. For example, LoRA, a low-rank adaptation method, can reduce the memory consumption to 12% of full fine-tuning, and Prefix Tuning, a continuous prompt optimization method, can reduce it to 20%.
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