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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• The rationale and constraints associated with full fine-tuning
• An overview of PEFT: objectives and advantages
• Industry applications and real-world use cases

LoRA (Low-Rank Adaptation)

• The concepts and intuition underpinning LoRA
• Implementing LoRA with Hugging Face and PyTorch
• Hands-on: Fine-tuning a model via LoRA

Adapter Tuning

• The mechanics of adapter modules
• Integrating adapters with transformer-based architectures
• Hands-on: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for fine-tuning
• Comparative strengths and limitations relative to LoRA and adapters
• Hands-on: 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 benchmarking experiments and results

Deploying Fine-Tuned Models

• Processes for saving and loading fine-tuned models
• Strategic considerations for deploying PEFT-based models
• Integration into production applications and pipelines

Best Practices and Extensions

• Combining PEFT with quantization and distillation techniques
• Application in low-resource and multilingual contexts
• Emerging trends and active research directions