Parameter-Efficient Fine-Tuning (PEFT) Techniques for LLMs Training Course

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The motivation for PEFT and the constraints of full fine-tuning
• Core objectives and advantages of the PEFT paradigm
• Industry applications and practical use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuitive understanding of LoRA
• Practical implementation using Hugging Face and PyTorch
• Lab exercise: Fine-tuning a model with LoRA

Adapter Tuning

• Mechanics of adapter modules
• Integration strategies for transformer-based architectures
• Lab exercise: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Comparative strengths and limitations versus LoRA and adapters
• Lab exercise: Executing Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

• Key metrics for assessing performance and efficiency
• Balancing trade-offs in training speed, memory consumption, and accuracy
• Interpreting benchmark experiments and results

Deploying Fine-Tuned Models

• Procedures for saving and loading optimized models
• Deployment considerations specific to PEFT-based solutions
• Seamless integration into existing applications and pipelines

Best Practices and Extensions

• Combining PEFT with quantization and knowledge distillation
• Application in low-resource and multilingual contexts
• Emerging trends and active areas of research