PEFT
Parameter-Efficient Fine-Tuning (PEFT) is a collection of training methods designed to fine-tune large foundation models by adapting only a tiny subset of additional parameters, while freezing the base model's weights.
Frequently Asked Questions
What are typical PEFT methods?▼
LoRA (Low-Rank Adaptation), Prefix Tuning, and Prompt Tuning are common PEFT approaches.
Why is PEFT critical for enterprise AI?▼
It slashes compute/storage costs by 95%+, enabling businesses to store lightweight task-specific adapters rather than separate multi-gigabyte models.
Quick Facts
- CategoryModel Training
- Key ApplicationLow-compute model adaptations, adapter model hosting, and custom domain specialization
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