Parameter-efficient Fine-tuning (PEFT)

Fine-Tuning Solutions

Definition

Parameter-efficient fine-tuning (PEFT) is a technique for adapting large pre-trained models to new tasks by updating only a small subset of parameters instead of the entire model.

Purpose

The purpose is to reduce computational cost and storage needs while maintaining strong task performance.

Importance

  • Makes fine-tuning feasible for organizations without massive resources.
  • Reduces carbon footprint compared to full model training.
  • Allows efficient task-switching in production.
  • Related to methods like LoRA and adapters.

How It Works

  1. Select a large pre-trained base model.
  2. Identify parameter subsets (e.g., low-rank adapters).
  3. Train only these subsets on target task data.
  4. Keep other parameters frozen.
  5. Deploy with minimal resource overhead.

Examples (Real World)

  • LoRA (Low-Rank Adaptation): widely used in fine-tuning LLMs.
  • Hugging Face PEFT library: efficient fine-tuning toolkit.
  • Google research: adapters for multilingual NLP tasks.

References / Further Reading

  • Hu et al. “LoRA: Low-Rank Adaptation of Large Language Models.” arXiv.
  • Houlsby et al. “Parameter-Efficient Transfer Learning for NLP.” ACL.
  • Hugging Face PEFT Documentation.