Fine-tuning
Parameter-Efficient Methods
38 SLIDES Part 4: Applications
The $50,000 Question: You have GPT-4 and 1,000 labeled examples. Full fine-tuning costs $50K and risks forgetting. LoRA costs $50 and preserves knowledge. What do you choose?
Prerequisites
- Week 6: Pre-trained models (BERT, GPT)
- Understanding of gradient descent and backpropagation
- Familiarity with overfitting and regularization
Overview
Adapt pre-trained models efficiently. LoRA, prompt tuning, and adapter methods.
Learning Objectives
- Compare full fine-tuning vs parameter-efficient methods (cost/performance)
- Implement LoRA (Low-Rank Adaptation) for efficient fine-tuning
- Design effective prompts for zero-shot and few-shot learning
- Understand catastrophic forgetting and how to prevent it
- Choose between fine-tuning, prompt engineering, and RAG approaches
Key Topics
LoRA
Prompt tuning
Adapters
Full fine-tuning
Key Concepts
Full fine-tuningUpdate all parameters ($50K+ cost, risk of forgetting)
LoRALow-rank adaptation matrices (0.1% parameters, similar quality)
Prompt engineeringZero-shot and few-shot prompting techniques
In-context learningLearning from examples in the prompt
Catastrophic forgettingLoss of pre-trained knowledge
PEFTParameter-Efficient Fine-Tuning methods
Key Visualizations
Adapter Architecture
Finetuning Pipeline
Lora Explanation
Finetuning