Training and fine-tuning AI models

From training your AI models at fine-tuning based on your business data, optimize your artificial intelligence for high-performance, customized and value-creating solutions.

In fact, a generalized model does not always provide the precision required for business realities. Fine-tuning and training enable us to transform a generic base into a more precise model. expert toolaligned with specific data, vocabulary and use cases. This is the key to reliable, contextualized and truly exploitable performance.

Corpus preparation : guarantee data quality through cleaning, annotation and structuring, to build a solid foundation.
Supervised fine-tuning : adapt models via LoRA, QLoRA, PEFT or full training, depending on available resources and strategic objectives.
Performance evaluation : measure relevance with precise indicators (perplexity, F1-score, exact match) to validate the robustness of results.
Training AI models : apply regularization and quality control techniques to guarantee fairness and reliability.
Deployment of specialized models : legal, medical, HR, finance... each sector benefits from a model adapted to its constraints, language and challenges.

Objective: move from generic artificial intelligence to tailor-made AI models, capable of generating relevant, reliable and directly applicable results.

The difference between AI model training and fine-tuning

L'AI model training is to build a model from scratch by having it learn patterns and relationships directly from large quantities of data, thus creating a general knowledge base suited to a given task.

Visit fine-tuningon the other hand, takes an already pre-trained model (often based on massive, generic data) and adapts it to a specific domain or use case, by re-training on a smaller dataset.

In a nutshell: l'training is the initial creation phase of a model, while fine-tuning is a customization that refines an existing model to precisely meet the needs of a particular company or sector.