Module 5: QLoRA and Quantization
Training Large Models on Consumer Hardware
This module covers QLoRA (Quantized LoRA), which combines 4-bit quantization with LoRA to enable fine-tuning of large models on consumer GPUs with minimal quality loss.
What You'll Learn
- Understand QLoRA and how it enables extreme memory efficiency
- Master 4-bit quantization using the bitsandbytes library
- Implement memory-efficient training techniques
- Optimize training for limited hardware
Module Structure
| Chapter | Topic | Duration |
|---|---|---|
| Chapter 1 | What is QLoRA? | 25 min |
| Chapter 2 | 4-bit Quantization | 30 min |
| Chapter 3 | Memory-Efficient Training | 30 min |
| Quiz | Module 5 Quiz | 30 min |
Key Concepts
- QLoRA: Quantized LoRA using 4-bit NF4 format
- NF4: 4-bit NormalFloat quantization format
- Double Quantization: Quantizing the quantization constants
- Gradient Checkpointing: Trading compute for memory
Estimated time: 2 hours