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LoRA, QLoRA & PEFT Methods

How LoRA works inside transformer layers, QLoRA for memory-efficient training, and the full PEFT method comparison with code examples.

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Production24 min readFirst readWhen to Fine-Tune: The Decision Framework

After this section you can

  • Explain what LoRA freezes and what it trains, and why the low-rank detour is so much cheaper
  • Choose between full fine-tuning, LoRA, QLoRA, and other PEFT methods for a given memory and quality budget
  • Set rank and target modules deliberately instead of copying defaults
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LoRA, QLoRA & PEFT Methods

How LoRA works inside transformer layers, QLoRA for memory-efficient training, and the full PEFT method comparison — with code examples and visual explanations.

LoRA — freeze the big matrix, train a tiny low-rank detour beside it
x W — pretrained weights d × d · FROZEN (no gradient) A r × d · trained B d × r · trained + h = Wx + (α/r)·BAx r ≪ d — the detour holds ~0.1–1% of the params, yet steers the whole layer · merge B·A into W for zero-overhead inference

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