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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Motivations for PEFT and the constraints of full fine-tuning
• Core objectives and advantages of the PEFT framework
• Real-world industry applications and use cases

LoRA (Low-Rank Adaptation)

• Conceptual understanding and intuition behind LoRA
• Implementing LoRA with Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model using LoRA

Adapter Tuning

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

Prefix Tuning

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

Evaluation and Comparison of PEFT Methods

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

Deployment of Fine-Tuned Models

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

Best Practices and Advanced Extensions

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