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
Computational Costs of Full Fine-Tuning
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- Adam: A Method for Stochastic Optimization, Diederik P. Kingma, Jimmy Ba, 2014 International Conference for Learning Representations DOI: 10.48550/arXiv.1412.6980 - Introduces the Adam optimizer, explaining its mechanism and the memory overhead of its state variables, which are critical for fine-tuning large models.
- Mixed Precision Training, Paulius Micikevicius, Sharan Narang, Jonah Alben, Gregory Diamos, Erich Elsen, David Garcia, Boris Ginsburg, Michael Houston, Oleksii Kuchaiev, Ganesh Venkatesh, Hao Wu, 2018 ICLR 2018 DOI: 10.48550/arXiv.1710.03740 - Pioneering paper on mixed precision training (FP16/BF16), detailing its benefits in reducing memory footprint and accelerating computation for deep learning models, directly relevant to the memory section.
- Training Deep Nets with Sublinear Memory Cost, Tianqi Chen, Bing Xu, Chiyuan Zhang, Carlos Guestrin, 2016 arXiv preprint arXiv:1604.06174 DOI: 10.48550/arXiv.1604.06174 - Presents gradient checkpointing, a technique to reduce activation memory during backpropagation by recomputing activations, which is crucial for training deep networks with limited GPU memory.
- DeepSpeed-ZeRO: Memory Optimization for Training Billions-Parameter Models, Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He, 2020 International Conference for High Performance Computing, Networking, Storage and Analysis (SC) (IEEE) DOI: 10.1109/SC41405.2020.00008 - Introduces the ZeRO memory optimization strategy, fundamental for efficient training of large-scale language models, addressing challenges in memory, compute, and distributed training setups.