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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.

SFT, LoRA, QLoRA & PEFT Methods

Fine-tuning techniques vary dramatically in compute cost, memory requirements, and effectiveness. Understanding the tradeoffs is essential.

Full Supervised Fine-Tuning (SFT)

Updates all model parameters. Maximum flexibility but highest cost and risk of catastrophic forgetting.

LoRA (Low-Rank Adaptation)

Freezes the original model and injects small trainable rank-decomposition matrices into attention layers. Typically trains only 0.1-1% of parameters.

First — Understanding the Transformer Layers You're Modifying

Before you can understand what LoRA changes, you need to understand the building blocks of a transformer. Every LLM (GPT, Claude, Llama, Gemini) is a stack of identical "transformer blocks." Each block has two main sub-components: an Attention mechanism and a Feed-Forward Network (MLP). Here's what each layer does and why it matters:

Inside One Transformer Block — Layer by Layer

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