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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Motivation and limitations of full fine-tuning
• Overview of PEFT: goals and benefits
• Applications and use cases in industry

LoRA (Low-Rank Adaptation)

• Concept and intuition behind LoRA
• Implementing LoRA using Hugging Face and PyTorch
• Hands-on: Fine-tuning a model with LoRA

Adapter Tuning

• How adapter modules work
• Integration with transformer-based models
• Hands-on: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Using soft prompts for fine-tuning
• Strengths and limitations compared to LoRA and adapters
• Hands-on: Prefix Tuning on an LLM task

Evaluating and Comparing PEFT Methods

• Metrics for evaluating performance and efficiency
• Trade-offs in training speed, memory usage, and accuracy
• Benchmarking experiments and result interpretation

Deploying Fine-Tuned Models

• Saving and loading fine-tuned models
• Deployment considerations for PEFT-based models
• Integrating into applications and pipelines

Best Practices and Extensions

• Combining PEFT with quantization and distillation
• Use in low-resource and multilingual settings
• Future directions and active research areas