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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Drivers and constraints associated with full fine-tuning
• Core objectives and advantages of PEFT
• Industrial applications and practical use cases

LoRA (Low-Rank Adaptation)

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

Adapter Tuning

• Functional mechanics of adapter modules
• Integration strategies within transformer-based architectures
• Practical session: Implementing Adapter Tuning on a transformer model

Prefix Tuning

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

Evaluation and Comparison of PEFT Methods

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

Deployment of Fine-Tuned Models

• Strategies for saving and loading fine-tuned weights
• Deployment considerations specific to PEFT-based models
• Integration into production applications and workflows

Best Practices and Advanced Extensions

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