Techniques

Fine-tuning

Définition

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.

Termes Connexes

Large Language Model (LLM)

AI models trained on vast amounts of text data that can understand and generate human-like text. Examples include GPT-4, Claude, and Llama. LLMs power modern chatbots, content generation, and code assistance tools.

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.

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.

PEFT (Parameter-Efficient Fine-Tuning)

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.

Services Connexes

Product Leadership Program

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