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

Foundations of Parameter-Efficient Fine-Tuning (PEFT)

• The rationale behind PEFT and the constraints of full fine-tuning
• Core objectives and strategic advantages of PEFT
• Real-world industry applications and use cases

LoRA (Low-Rank Adaptation)

• Underlying concepts and intuitive understanding of LoRA
• Technical implementation using Hugging Face and PyTorch
• Practical exercise: Fine-tuning a model via LoRA

Adapter Tuning

• Mechanisms of adapter modules
• Seamless integration with transformer-based architectures
• Practical exercise: Applying Adapter Tuning to transformer models

Prefix Tuning

• Leveraging soft prompts for targeted fine-tuning
• Analyzing strengths and limitations against LoRA and adapters
• Practical exercise: Executing Prefix Tuning on LLM tasks

Assessment and Comparative Analysis 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 results

Deployment of Fine-Tuned Models

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

Advanced Practices and Future Extensions

• Enhancing PEFT through quantization and distillation
• Application in low-resource and multilingual environments
• Emerging trends and active areas of research