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
• PEFT overview: objectives and advantages
• Industrial applications and use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuition behind LoRA
• LoRA implementation using Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model using LoRA

Adapter Tuning

• Mechanics of adapter modules
• Integration with transformer-based architectures
• Practical exercise: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for fine-tuning
• Advantages and limitations relative to LoRA and adapters
• Practical exercise: Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

• Metrics for assessing performance and efficiency
• Trade-offs regarding training speed, memory consumption, and accuracy
• Conducting benchmark experiments and interpreting results

Deploying Fine-Tuned Models

• Techniques for saving and loading fine-tuned models
• Deployment considerations specific to PEFT-based models
• Integration into applications and data pipelines

Best Practices and Extensions

• Combining PEFT with quantization and distillation
• Applications in low-resource and multilingual contexts
• Future trends and active areas of research