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

Foundations of Parameter-Efficient Fine-Tuning (PEFT)

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

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuitive understanding of LoRA
• Practical implementation of LoRA via Hugging Face and PyTorch
• Live workshop: Fine-tuning a model using LoRA

Adapter Tuning

• Mechanisms and functionality of adapter modules
• Integration strategies for transformer-based architectures
• Live workshop: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for the fine-tuning process
• Analyzing strengths and limitations relative to LoRA and adapters
• Live workshop: Implementing Prefix Tuning for an LLM task

Assessment and Comparative Analysis of PEFT Methods

• Key metrics for gauging model performance and efficiency
• Trade-offs regarding training speed, memory consumption, and accuracy
• Interpreting benchmarking experiments and experimental results

Deployment Strategies for Fine-Tuned Models

• Procedures for saving and loading fine-tuned weights
• Key considerations for deploying PEFT-based solutions
• Seamless integration into existing applications and pipelines

Best Practices and Advanced Extensions

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