Fine-tuning
The process of adapting a pre-trained AI model to a specific domain or task by continuing training on targeted data.
The process of adapting a pre-trained AI model to a specific domain or task by continuing training on targeted data.
Définition détaillée
Fine-tuning involves taking a pre-trained AI model (like an LLM) and continuing its training on a smaller, specialized dataset. This adapts the model to a specific domain (legal, medical, technical), improves performance on particular tasks, or teaches specific response formats. Techniques like LoRA (Low-Rank Adaptation) and QLoRA enable efficient fine-tuning with limited resources.
Cas d'usage
Specialized legal model: fine-tuning a general LLM on 10,000 sample contracts and court decisions to obtain an assistant capable of drafting and analyzing contractual clauses with 95% accuracy.
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Questions fréquentes
How much data is needed for effective fine-tuning?
It depends on the task: a few hundred examples suffice for style adaptation, but several thousand are recommended for specific domain expertise.
Is fine-tuning expensive?
Modern techniques (LoRA, QLoRA) enable fine-tuning for tens to hundreds of euros, much less than training a full model.