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IA & Data
LLM Fine-Tuning – Adapting a Model to Your Business Data – Advanced
3 jour(s) • 21h
Description
Master advanced techniques for fine-tuning open-source large language models to adapt an LLM to a specific business domain. The training covers the construction of a complete fine-tuning pipeline, including model selection, data preparation, training with PEFT methods (LoRA, QLoRA), performance evaluation, and deployment of a specialized model.
Learning Objectives
- Understand the architectures and limitations of major open-source LLMs.
- Select an adaptation strategy suited to a business use case.
- Prepare a training dataset for a specialized LLM.
- Implement fine-tuning using Hugging Face Transformers and PEFT.
- Use LoRA and QLoRA techniques to optimize training.
- Evaluate the performance of a fine-tuned model.
- Deploy a specialized model in an application environment.
Target Audience
Data scientists
Machine learning engineers
NLP engineers
AI engineers
Data/IA architects
Developers specialized in generative AI
Prerequisites
Strong proficiency in Python
Knowledge of machine learning and deep learning
Experience with Transformer architectures
Familiarity with the Hugging Face ecosystem
Program Outline
Informations
Duration
3 jour(s)
21h
Tarif
Sur demande
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