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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

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

LoRA (Low-Rank Adaptation)

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

Adapter Tuning

• Mechanisms of adapter modules
• Integrating adapters into transformer-based architectures
• Practical session: Applying Adapter Tuning to a transformer model

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Comparative strengths and limitations versus LoRA and adapters
• Practical session: Applying Prefix Tuning to an LLM task

Evaluating and Comparing PEFT Methods

• Key metrics for assessing performance and efficiency
• Balancing training speed, memory consumption, and accuracy
• Conducting benchmarks and interpreting results

Deploying Fine-Tuned Models

• Techniques for saving and loading adapted models
• Considerations for deploying PEFT-based solutions
• Integration into broader applications and pipelines

Best Practices and Extensions

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