Advanced LoRA and PEFT Techniques for LLM Fine-Tuning
Chapter 1: Revisiting Fine-Tuning and the Need for Efficiency
Computational Costs of Full Fine-Tuning
The Parameter Efficiency Imperative
Mathematical Preliminaries: Singular Value Decomposition
Taxonomy of Parameter-Efficient Fine-Tuning Methods
Chapter 2: Low-Rank Adaptation (LoRA) In Depth
The LoRA Hypothesis: Low Intrinsic Rank of Adaptation
Mathematical Formulation of LoRA
Decomposing Weight Update Matrices
Rank Selection Strategies
Scaling Parameter Alpha
Implementing LoRA Layers
Integrating LoRA into Transformer Architectures
Hands-on Practical: Applying Basic LoRA
Chapter 3: Survey of PEFT Methodologies
Adapter Tuning: Architecture and Mechanisms
Adapter Tuning Implementation Details
Prefix Tuning: Conditioning via Continuous Prefixes
Prompt Tuning and P-Tuning Variations
Comparative Analysis: Parameters vs Performance Trade-offs
Memory and Computational Footprints
Hands-on Practical: Implementing Adapter Tuning
Chapter 4: Advanced LoRA Implementations and Variants
LoRA Initialization Strategies
Merging LoRA Weights Post-Training
Quantized LoRA (QLoRA): Principles
QLoRA Implementation Details
Paged Optimizers for Memory Efficiency
Combining LoRA with Other PEFT Approaches
Hands-on Practical: Implementing QLoRA
Chapter 5: Optimization, Deployment, and Practical Considerations
Infrastructure Requirements for PEFT Training
Optimizers and Learning Rate Schedulers for PEFT
Techniques for Multi-Adapter / Multi-Task Training
Debugging PEFT Implementations
Performance Profiling PEFT Training and Inference
Distributed Training Strategies with PEFT
Serving Models with PEFT Adapters
Hands-on Practical: Fine-tuning with Multiple LoRA Adapters
Chapter 6: Evaluating PEFT Performance and Limitations
Standard Metrics for PEFT Evaluation
Benchmarking PEFT against Full Fine-Tuning
Analyzing Robustness and Generalization
Investigating Catastrophic Forgetting
Computational Cost Analysis Revisited
Current Limitations and Open Research Questions
Revisiting Fine-Tuning and the Need for Efficiency
Fine-tuning large language models (LLMs) is a standard technique for adapting them to specific tasks. However, modifying all parameters of models containing billions of parameters, often represented as a weight matrix W, incurs substantial computational and memory costs. This chapter examines these challenges inherent in full fine-tuning.
You will learn about:
- The specific memory, compute, and storage requirements of full fine-tuning.
- The factors making parameter-efficient alternatives necessary.
- The mathematical background of Singular Value Decomposition (SVD), relevant for understanding methods like LoRA, often used for low-rank approximations like M≈UΣVT.
- A classification of different Parameter-Efficient Fine-Tuning (PEFT) approaches.
This foundational knowledge will illustrate why PEFT methods have become essential and introduce the categories of techniques explored throughout this course.
Sections
1.1 Computational Costs of Full Fine-Tuning
1.2 The Parameter Efficiency Imperative
1.3 Mathematical Preliminaries: Singular Value Decomposition
1.4 Taxonomy of Parameter-Efficient Fine-Tuning Methods
© 2025 ApX Machine Learning