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.
Reuse: licensed under CC BY 4.0 — quoting, summarizing, and republishing is permitted with attribution.
Cite as: “LoRA Fine-Tuning Illustrated — Low-Rank Adaptation Theory,” AI Dev Hub, https://aidevhub.net/blog/lora-finetuning-illustrated
Markdown for AI/LLM consumption: /blog/lora-finetuning-illustrated.md · full corpus: /llms-full.txt
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