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

Introduction to Parameter-Efficient Fine-Tuning (PEFT)

• Why full fine-tuning is often impractical: motivation and limitations
• PEFT at a glance: objectives and key advantages
• Real-world industry applications and use cases

LoRA (Low-Rank Adaptation)

• The conceptual basis 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 effective fine-tuning
• Comparative analysis: strengths and limitations relative to LoRA and adapters
• Practical exercise: Prefix Tuning for specific LLM tasks

Evaluating and Comparing PEFT Methods

• Key metrics for assessing performance and efficiency
• Balancing training speed, memory consumption, and model accuracy
• Conducting benchmark experiments and interpreting results

Deploying Fine-Tuned Models

• Techniques for saving and loading fine-tuned weights
• Considerations for deploying PEFT-based models
• Integration into production applications and workflows

Best Practices and Advanced Extensions

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