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
Infrastructure Requirements for PEFT Training
Was this section helpful?
- LoRA: Low-Rank Adaptation of Large Language Models, Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, 2021 arXiv preprint arXiv:2106.09685 DOI: 10.48550/arXiv.2106.09685 - Introduces the LoRA method, explaining how it reduces trainable parameters and associated resource needs for fine-tuning.
- QLoRA: Efficient Finetuning of Quantized LLMs, Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, Luke Zettlemoyer, 2023 arXiv preprint arXiv:2305.14314 DOI: 10.48550/arXiv.2305.14314 - Details 4-bit NormalFloat (NF4) quantization, double quantization, and paged optimizers, which reduce VRAM requirements for fine-tuning.
- Parameter-Efficient Fine-Tuning (PEFT) library, Hugging Face, 2024 (Hugging Face) - Official guide for the
peftlibrary, offering practical information on implementation, API usage, and compatibility with Hugging Face models. - Accelerate training with 🤗 Accelerate, Hugging Face, 2024 - Official documentation for Hugging Face Accelerate, a library that assists with device placement, mixed precision, and distributed training for PyTorch.