Remember PEFT
Full name: Parameter-Efficient Fine-Tuning
Memory aid: Parameters are used Efficiently for Fine Tuning.
Parameter efficiency does not automatically imply higher model quality.
Study area: AI & machine learning · Batch 4
Relevant study for: Azure AI Fundamentals AI-901 · Azure AI Apps and Agents Developer AI-103 · Azure AI Fundamentals (retired) AI-900 · Azure AI Engineer (retired) AI-102
Practice with exam flashcards →AI Models & Assistants / Knowledge library
PEFT (Parameter-Efficient Fine-Tuning)
Parameter efficient fine tuning with small trainable components
Why learn PEFT (Parameter-Efficient Fine-Tuning)?
Useful for specializing large models with fewer trainable parameters and smaller task-specific checkpoints. The underlying base model is still needed for inference.
The problem it solves
Reduces the training and checkpoint-storage burden of adapting a large pretrained model to a task.
Where it came from
Parameter-efficient fine-tuning adapts a pretrained model while updating only a small subset of parameters or additional trainable components. LoRA and prompt tuning are examples within this broader family; Hugging Face PEFT is a library implementing multiple such methods.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Count trainable parameters and compare them with total parameters.
- Explain the difference between LoRA and the broader PEFT family.
- Contrast learned soft prompts with ordinary text prompts.
- Save an adapter and identify the base model needed to reload it.
- Compare task accuracy and training memory against full fine-tuning.
Documentation and sources
- Hugging Face PEFT documentation (huggingface.co)