Fine-Tuning & PEFT
Parameter-Efficient Fine-Tuning
How to Make Smart AI Models Smarter — Without Breaking Your Computer
- What is Fine-Tuning?
Imagine a brilliant student who has read millions of books — that's the pre-trained model. Now you want that student to become a doctor, so you give them medical textbooks and practice exams. That extra, focused training on one subject is Fine-Tuning.
Key Points
Why do it? A general model knows a little about everything; a fine-tuned model becomes an expert in ONE area.
Cost: full fine-tuning is expensive — it needs a large amount of GPU memory.
- The Problem: Fine-Tuning Is Too Expensive
Full fine-tuning is like repainting your entire house just to change the color of one room — it wastes time, money, and paint.
The Numbers
GPT-3 has 175 billion parameters.
Full fine-tuning means updating ALL 175 billion parameters.
This requires multiple high-end GPUs, costing thousands of dollars per run.
- Solution: PEFT (Parameter-Efficient Fine-Tuning)
Instead of changing the entire model, PEFT changes only a small part of it — like putting a sticky note with new information on a page, instead of rewriting the whole book.
Benefits
Uses about 99% less memory
Trains roughly 10x faster
Can store multiple small 'adapters' for different tasks
No loss in performance — sometimes even better!
- LoRA (Low-Rank Adaptation) ⭐ Most Popular
Think of a weight matrix — a big table of numbers — as a map. LoRA says: instead of redrawing the whole map, just add a small overlay on top of it.
Key Terms
Rank (r) — a small number (e.g. 8) that controls how much the model is allowed to change.
A — a small matrix, randomly initialized.
B — a small matrix that starts as all zeros.
Only A and B are trained — far fewer parameters than the full weight matrix.
Example Numbers
That's about 20,000x smaller — while keeping comparable performance! 🎉