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#low-rank

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01 ·Linear Algebra·★ MEMBER·PAPER·10 min read Singular Value Decomposition and Low-Rank Approximation — the Math Behind LoRA Starting from the 'rotate, stretch, rotate' picture, this article builds Singular Value Decomposition (SVD) from zero: matrices as stacks of rank-1 layers, why real-world data needs only a few of them, and how that single fact lets LoRA fine-tune a giant model with 0.4% of the parameters.