LoRA (Low-Rank Adaptation)
An efficient fine-tuning method that trains a small add-on layer instead of the whole model, saving time and compute.
It makes customizing an open-weight model practical without the cost of full retraining.
Frequently asked questions
What is the cheapest way to customize an open-weight model?
LoRA is one of the most practical options. Instead of retraining the whole model, it trains a small add-on layer, which saves a lot of time and compute while still adapting the model to your needs.
How is LoRA different from full fine-tuning?
Full fine-tuning adjusts the entire model, which is slow and costly, while LoRA trains only a small add-on and leaves the base model untouched. You get much of the benefit of customization without the heavy price of full retraining.
Do I need LoRA to change how a model writes?
Often not. If prompting or few-shot examples get you consistent results, you may not need any training at all. LoRA is worth it when prompting alone cannot lock in the behavior and you are working with an open-weight model you can adapt.
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