AI Course 3: “Smart Fine-Tuning: Adapt LLMs without Burning Your GPU”

QLoRA enables efficient fine-tuning of large language models on low-memory GPUs by combining 4-bit quantization with low-rank adapters, reducing VRAM usage while preserving performance.

🧭 Course Structure

• Module 1: What is Fine-Tuning and Why Is It So Expensive?
• Module 2: PEFT — The Efficient Fine-Tuning Paradigm
• Module 3: LoRA — Low-Rank Adaptation
• Module 4: QLoRA — High-Performance Quantized Fine-Tuning
• Module 5: Practical Configuration — Hyperparameters, target_modules, and Environment
• Module 6: Dataset Preparation and Instruction Format
• Module 7: Training Configuration with TRL (Transformer Reinforcement Learning)
• Module 8: Monitoring Training and Evaluation
• Module 9: Resource Management and Common Issues
• Module 10: Saving, Loading, and Merging LoRA/QLoRA Adapters
• Module 11: Final Integrated Project — Fine-Tuning Qwen2.5-0.5B for Product Description Generation