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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Drivers and constraints associated with full fine-tuning
• Overview of PEFT objectives and its key advantages
• Real-world industry applications and use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuitive understanding of LoRA
• Implementation of LoRA leveraging Hugging Face and PyTorch
• Practical session: Fine-tuning a model via LoRA

Adapter Tuning

• Mechanics of adapter modules
• Integration strategies with transformer-based architectures
• Practical session: Implementing Adapter Tuning on a transformer model

Prefix Tuning

• Utilization of soft prompts for effective fine-tuning
• Comparative strengths and limitations against LoRA and adapters
• Practical session: Applying Prefix Tuning to an LLM task

Assessment and Comparison of PEFT Methods

• Key metrics for gauging performance and efficiency
• Navigating trade-offs in training velocity, memory consumption, and accuracy
• Conducting benchmark experiments and interpreting outcomes

Deployment of Fine-Tuned Models

• Strategies for saving and retrieving fine-tuned models
• Deployment considerations specific to PEFT-based architectures
• Seamless integration into applications and production pipelines

Best Practices and Advanced Extensions

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