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Course Outline

Overview of Parameter-Efficient Fine-Tuning (PEFT)

  • Rationale and constraints associated with comprehensive model fine-tuning
  • Strategic objectives and operational advantages of PEFT methodologies
  • Relevant applications and sector-specific use cases for government

Low-Rank Adaptation (LoRA)

  • Theoretical framework and operational logic underlying LoRA
  • Implementation protocols using Hugging Face and PyTorch frameworks
  • Practical exercise: Executing model fine-tuning via LoRA

Adapter Tuning

  • Operational mechanics of adapter modules within model architectures
  • Integration protocols for transformer-based systems
  • Practical exercise: Applying Adapter Tuning to transformer models

Prefix Tuning

  • Utilization of continuous inputs for fine-tuning processes
  • Comparative analysis of performance and constraints relative to LoRA and adapters
  • Practical exercise: Implementing Prefix Tuning for large language model tasks

Evaluation and Comparison of PEFT Methodologies

  • Performance and efficiency assessment metrics
  • Analysis of trade-offs among training latency, resource consumption, and accuracy
  • Conducting benchmarking experiments and interpreting findings

Deployment of Fine-Tuned Models

  • Procedures for saving and loading fine-tuned model weights
  • Operational considerations for deploying PEFT-based models in government environments
  • Integration strategies for applications and data pipelines

Best Practices and Technical Extensions

  • Synthesizing PEFT with quantization and distillation techniques
  • Application in low-resource and multilingual contexts
  • Emerging research trends and future development pathways

Summary and Recommended Next Steps

Requirements

To ensure effective implementation of advanced computational solutions for government, participants are expected to possess a foundational knowledge of machine learning principles and hands-on experience utilizing large language models. Proficiency in Python and PyTorch is required to support these technical workflows. This curriculum is designed specifically for data scientists and artificial intelligence engineers who oversee data-driven initiatives and system integration within public sector environments.
 14 Hours

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