LoRA (Low-Rank Adaptation)
Definition
LoRA, or Low-Rank Adaptation, is a technique for fine-tuning large AI models without updating every parameter. Instead, it inserts small trainable matrices into the model. This means developers can customize powerful models like LLMs using fewer resources and less compute.
Example
“You can use LoRA to fine-tune a language model for legal writing—without retraining the whole model.”
How It’s Used in AI
LoRA is widely used for personalizing open-weight models, building domain-specific assistants, and reducing training costs. It’s a popular technique in Hugging Face’s PEFT (Parameter-Efficient Fine-Tuning) library and is often combined with fine-tuning and prompt tuning.
Brief History
Introduced by Microsoft Research in 2021, LoRA quickly became a go-to method for low-resource customization of LLMs like LLaMA, Mistral, and Falcon.
Key Tools or Models
Used in LLaMA, Mistral, Falcon, and Gemma
Libraries: Hugging Face PEFT, QLoRA
Deployed in use cases like healthcare bots, coding copilots, and custom assistants
Pro Tip
LoRA adapters are lightweight and can be shared separately from the model—perfect for open-source communities and fast deployment.