Advanced LoRA and PEFT Techniques for LLM Fine-Tuning
Master advanced techniques for efficient Large Language Model (LLM) fine-tuning using Low-Rank Adaptation (LoRA) and other Parameter-Efficient Fine-Tuning (PEFT) methods. This course covers the theoretical underpinnings, complex implementations, and optimization strategies for state-of-the-art LLM adaptation. Gain proficiency in applying methods like LoRA, QLoRA, Adapter Tuning, and more, focusing on performance, memory efficiency, and practical application challenges.
Prerequisites: Strong understanding of LLMs (Transformers, attention mechanisms), deep learning concepts (backpropagation, optimizers), extensive experience with Python and ML frameworks (PyTorch/TensorFlow), and familiarity with standard LLM fine-tuning procedures.
Level: Advanced
What You'll Learn
PEFT Fundamentals
Analyze the limitations of full fine-tuning and the mathematical principles behind parameter-efficient approaches.
LoRA Implementation
Implement and configure LoRA layers within standard Transformer architectures.
Advanced LoRA Variants
Implement and evaluate advanced techniques like QLoRA and LoRA merging strategies.
Comparative PEFT Analysis
Compare and contrast various PEFT methods (Adapters, Prefix Tuning, Prompt Tuning) based on performance and computational cost.
PEFT Optimization
Optimize PEFT training workflows, including infrastructure considerations, optimizers, and debugging strategies.
Performance Evaluation
Evaluate the performance, robustness, and limitations of different PEFT methods on downstream tasks.
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