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
Memory and Computational Footprints
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- 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 DOI: 10.48550/arXiv.2106.09685 - Introduces LoRA, a method that adapts pre-trained models by injecting trainable low-rank matrices into the transformer layers, significantly reducing the number of trainable parameters and memory footprint.
- Parameter-Efficient Transfer Learning for NLP, Neil Houlsby, Andrei Giurgiu, Stanislau Padolski, Quentin de Latour, Max Vladutu, Albert Verga, Quincy Hatcliff, Jason Riesa, Anna Schiff, Shauna Horn, Melvin Johnson, George Dahl, Orhan Firat, 2019 Proceedings of the 36th International Conference on Machine Learning (ICML), Vol. 97 - Proposes adapter modules, small neural network layers inserted between transformer layers, enabling efficient fine-tuning with a small number of additional parameters.
- Prefix-Tuning: Optimizing Continuous Prompts for Generation, Xiang Lisa Li, Percy Liang, 2021 Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Vol. Volume 1: Long Papers (Association for Computational Linguistics) DOI: 10.18653/v1/2021.acl-long.353 - Introduces prefix-tuning, which adds a small sequence of trainable vectors (the prefix) to the input of each transformer layer, freezing the base model and reducing trainable parameters.
- The Power of Scale for Parameter-Efficient Prompt Tuning, Brian Lester, Rami Al-Rfou, Noah Constant, 2021 Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Association for Computational Linguistics) DOI: 10.18653/v1/2021.emnlp-main.243 - Explores prompt tuning, where only a small set of trainable tokens (soft prompts) are optimized, achieving performance comparable to full fine-tuning for large models with minimal parameter overhead.