Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training LoRA Fine-Tuning - Customizing AI LLMs in 2026 training are: Python basics, numpy/pandas, and deep learning fundamentals. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LoRA Fine-Tuning - Customizing AI LLMs in 2026 training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LoRA Fine-Tuning - Customizing AI LLMs… | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training LoRA Fine-Tuning - Customizing AI LLMs in 2026 training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training LoRA Fine-Tuning - Customizing AI LLMs in 2026 training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training LoRA Fine-Tuning - Customizing AI LLMs in 2026 training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master LoRA principles for fine-tuning professional models

Develop skills in efficient adaptation of LLMs in enterprise settings

Implement LoRA with PEFT and Hugging Face for certified projects

Design LoRA fine-tunings on real and optimized datasets

Deploy customized LoRA models in certified production environments

Optimize GPU resources via LoRA for professional gains

Training program

Module 1Fundamentals: Understanding LoRA Fine-Tuning (PEFT, Hugging Face, PyTorch)

Discovery of key LoRA concepts to adapt LLMs without full retraining, installation of the environment with PEFT and transformers libraries, first exercises on a BERT-like model to customize simple NLP tasks, analysis of low-rank matrices and their impact on GPU resources, creation of a first functional LoRA script with validation on an enterprise dataset, immediate trainer feedback to consolidate the basics.

Module 2Practice: Applying LoRA Fine-Tuning to LLMs (datasets, hyperparameters, evaluation)

Practical fine-tuning of an LLM like Llama on a custom text dataset for an enterprise chatbot, configuration of LoRA hyperparameters (rank, alpha), use of Hugging Face Trainer to accelerate training, evaluation tests with perplexity and BLEU metrics, collaborative exercises on real customer automation cases, generation of performance reports and iterative adjustments to maximize professional accuracy.

Module 3Deployment: Optimize and Deploy LoRA Fine-Tuning (ONNX, AI Servers, Monitoring)

Export of optimized LoRA models to ONNX for rapid deployment, integration with FastAPI or Docker for enterprise APIs, monitoring of production performance via Weights & Biases, exercises on scaling LoRA for large data volumes in 2026, creation of tailored CI/CD pipelines, finalization of a complete red thread project with certified documentation and maintenance plan for immediate implementation.