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

Foundations of Parameter-Efficient Fine-Tuning (PEFT)

• Addressing the constraints and motivations behind full fine-tuning
• Core objectives and strategic advantages of PEFT
• Industrial applications and real-world use cases

LoRA (Low-Rank Adaptation)

• Theoretical concepts and intuitive mechanics of LoRA
• Building LoRA implementations with Hugging Face and PyTorch
• Practical session: Fine-tuning a model using LoRA

Adapter Tuning

• Operational mechanics of adapter modules
• Integration strategies for transformer architectures
• Practical session: Implementing Adapter Tuning on a transformer model

Prefix Tuning

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

Performance Evaluation and Comparison of PEFT Methods

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

Deployment of Fine-Tuned Models

• Techniques for persisting and loading adapted models
• Strategic considerations for deploying PEFT-enhanced models
• Seamless integration into production applications and pipelines

Advanced Practices and Future Extensions

• Combining PEFT with quantization and knowledge distillation
• Optimizing for low-resource and multilingual environments
• Exploring emerging research directions and future trends