PEFT (Parameter-Efficient Fine-Tuning)
What is PEFT?
Parameter-Efficient Fine-Tuning (PEFT) is a set of techniques to fine-tune Large Language Models (LLMs) without retraining all parameters. Instead of updating billions of weights, PEFT updates only a small fraction (often < 1%) of added parameters.
The Core Idea: Freeze the massive pre-trained model and only train small adapter layers.
Benefits:
- Lower Hardware Cost: Can run on consumer GPUs (e.g., a single RTX 4090 instead of an A100 cluster).
- Storage Efficiency: "Adapters" are small files (MBs) vs full models (GBs).
- Multi-Tenancy: You can serve one base model and swap small adapters for different users/tasks on the fly.
LoRA (Low-Rank Adaptation)
LoRA is the most popular PEFT technique.
- How it works: It injects small "rank decomposition matrices" into the model and trains only those.
- Analogy: Imagine editing a book. Instead of rewriting the whole book (Full Fine-Tuning), you just write your edits on sticky notes and stick them on the pages (LoRA). When reading, you read the original page + the sticky note.
When Companies Use PEFT
- Cost-Effective Customization: When they need a custom model for a specific task (e.g., "SQL Generator") but can't afford to train a full 70B parameter model.
- Privacy/On-Premise: Running fine-tuned open-source models (Llama 3, Mistral) on local hardware.
- Personalized AI: Creating thousands of user-specific models (e.g., one style adapter per user) that share one base model.
Frontend Relevance
Running Local LLMs
If you use tools like Ollama or LM Studio, you are often downloading "quantized" models or applying LoRA adapters. Understanding PEFT helps you understand why you can run powerful AI on your MacBook.