AI030
Generative AI Foundations in Python
Fine-Tuning Techniques and Parameter-Efficient Learning
Learning Objectives
- Differentiate between full fine-tuning and parameter-efficient fine-tuning (PEFT) paradigms.
- Master the implementation details of Low-Rank Adaptation (LoRA) and bottleneck adapters.
- Evaluate the impact of prefix tuning and prompt tuning on model convergence.
- Analyze memory-performance trade-offs in resource-constrained fine-tuning environments.