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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Drivers for PEFT and the constraints of full fine-tuning
• Overview of PEFT: objectives and key advantages
• Real-world industry applications and use cases

LoRA (Low-Rank Adaptation)

• Core concepts and intuitive understanding of LoRA
• Implementing LoRA with Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model using LoRA

Adapter Tuning

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

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Comparative strengths and limitations versus LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Assessment and Comparison of PEFT Methods

• Key metrics for gauging performance and efficiency
• Balancing trade-offs in training speed, memory consumption, and accuracy
• Conducting benchmarking experiments and interpreting outcomes

Deployment of Fine-Tuned Models

• Strategies for saving and loading fine-tuned models
• Key considerations when deploying PEFT-based models
• Integration into production applications and pipelines

Best Practices and Advanced Extensions

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