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

Foundations of Parameter-Efficient Fine-Tuning (PEFT)

• The rationale for PEFT and the constraints of full fine-tuning
• Overview of PEFT objectives and advantages
• Industrial 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 exercise: Fine-tuning a model using LoRA

Adapter Tuning

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

Prefix Tuning

• Leveraging soft prompts for model adaptation
• Advantages and constraints when compared with LoRA and adapters
• Practical exercise: Executing Prefix Tuning on an LLM task

Assessment and Comparison of PEFT Methods

• Key metrics for evaluating performance and efficiency
• Trade-offs regarding training speed, memory consumption, and accuracy
• Benchmarking strategies and interpreting experimental results

Deploying Fine-Tuned Models

• Processes for saving and loading fine-tuned models
• Considerations for deploying PEFT-based models
• Integration into existing applications and pipelines

Best Practices and Advanced Extensions

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