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AI GlossaryLTechnical words in the news

LoRA (Low-Rank Adaptation)

Low-Rank Adaptation

A low-cost fine-tuning method that trains a small "add-on patch" instead of the whole model — cheap enough to run on a personal PC.

In plain words

Low-Rank Adaptation — a fine-tuning technique that doesn't retrain the whole model. Instead, it trains a small "patch" (an adapter) and attaches it to the original model. It's like remodeling a single room instead of reconstructing the entire building, cutting costs to a tiny fraction of the original.

This is what made it possible to customize models even on a personal PC. It's especially popular in image-generation communities, where people create and share LoRAs for specific art styles or specific characters. That same popularity is why disputes over style imitation and likeness rights tend to swirl around this term.

Fine-tuning vs LoRA: what's the difference

Trains only a small added piece, Retrains the entire model

LoRA is itself a type of fine-tuning, so asking 'which one should I use' is a bit of a false choice. Fine-tuning is the broad category, and LoRA is a cheap way of doing it. When articles today say a model was 'fine-tuned,' it usually actually means LoRA.

AspectLoRA (Low-Rank Adaptation)Fine-tuning
What gets trainedOnly a small added pieceAll the numbers inside the model
CostHas come down to the point where a personal PC can handle itRequires enterprise-grade hardware
OutputThe original stays as is; a single small file is producedAn entirely new full model is produced
SwappingOnly the piece is swapped in and out; multiple can be used interchangeablyThe whole model has to be replaced
AnalogyAdding a patch to existing clothesMaking a new set of clothes

Rule of thumbFine-tuning is the goal, and LoRA is a cheap way to achieve that goal — it's not a matter of choosing one or the other.

See also

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