Techniques

PEFT (Parameter-Efficient Fine-Tuning)

Définition

A family of techniques—including LoRA, prefix tuning, and adapters—that adapt large pre-trained models to new tasks by training only a small fraction of parameters. PEFT dramatically reduces compute and storage costs compared to full fine-tuning.

Termes Connexes

LoRA (Low-Rank Adaptation)

A parameter-efficient fine-tuning method that injects trainable low-rank matrices into transformer layers instead of updating all model weights. LoRA enables cost-effective domain adaptation of large models on modest hardware, making enterprise fine-tuning accessible without full GPU clusters.

Fine-tuning

The process of taking a pre-trained AI model and further training it on a specific dataset to adapt it for a particular task or domain. Fine-tuning can deliver significantly better performance than prompting alone for specialised enterprise workflows.

Transfer Learning

A technique where a model pre-trained on one task or dataset is adapted for a different but related task. Transfer learning dramatically reduces the data and compute needed to build performant models for specialised domains like legal, medical, or industrial applications.

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