Explore a 28-minute technical video analyzing MIT's research on the fundamental differences between Parameter Efficient Fine-Tuning (PEFT) Low-Rank Adaptation (LoRA) and full fine-tuning methods for language models. Dive deep into the spectral properties and structural distinctions between these approaches, understanding how LoRA introduces "intruder dimensions" - new high-ranking singular vectors orthogonal to pre-trained weights - while full fine-tuning makes subtle adjustments to existing singular vectors. Learn about the trade-offs between computational efficiency and model adaptability, examining how LoRA's rank settings influence generalization behavior and retention of pre-trained knowledge. Follow along with detailed segments covering structural differences, rank versus size relationships, SVD spectral properties, weight tensor update functions, and practical examples, concluding with insights on LoRA rank stabilization and essential takeaways for implementing these fine-tuning methods.
Overview
Syllabus
PEFT LoRA
Structural Differences
Rank vs Size
Spectral properties in SVD
LoRA introduces new vectors
MIT pre-print October 28
Example simplification
Weight tensor update functions
LoRA Rank stabilization
Key Takeaways
Taught by
Discover AI
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