LoRA Fine-Tuning Illustrated — Low-Rank Adaptation Theory
Visual explanation of LoRA parameter-efficient fine-tuning covering rank decomposition, adapter design, scaling factors, and practical implementation.
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LoRA Fine-Tuning Illustrated
LoRA enables LLM fine-tuning with 10,000x fewer trainable parameters by freezing base weights and injecting low-rank matrices.
How It Works
Instead of updating W (d x k), LoRA learns A (d x r) and B (r x k) where r=8 is typical. Forward: h = Wx + BAx. The rank r controls expressiveness vs efficiency.
Why It Works
Pre-trained LLMs have low intrinsic rank — task-specific updates lie in a low-dimensional subspace.
Multi-Task
LoRA adapters swap at inference without reloading base models, enabling cost-effective multi-tenant serving.
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