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 - Effectively Customize AI Models training are: Mastery of Python, PyTorch, and basics of deep learning and transformers. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LoRA Fine-Tuning - Effectively Customize AI Models training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training LoRA Fine-Tuning - Effectively Customiz... | 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 - Effectively Customize AI Models 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 - Effectively Customize AI Models 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 - Effectively Customize AI Models 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 fine-tuning to adapt AI models in a business context

Develop skills in optimizing large language models

Implement LoRA on professional datasets using PEFT

Design efficient and cost-effective fine-tuning pipelines

Optimize generative AI models for certified applications

Deploy LoRA fine-tuned models in scalable production

Training program

Module 1Fundamentals of LoRA Fine-Tuning and Preparation

Introduction to LoRA and its principles. Differences with full fine-tuning. Installation of the Hugging Face and PEFT environment. Preparation of datasets for professional fine-tuning. Hands-on practice on a base model like Llama. Exercises on concrete business cases. Start of the red thread project: adaptation of an AI model. Understanding low-rank matrices. Management of initial hyperparameters. Preliminary tests to validate the configuration.

Module 2Advanced Hands-On LoRA Fine-Tuning

LoRA implementation on advanced transformers. Fine-tuning with QLoRA for memory efficiency. Management of tokens and custom collators. Training on professional multilingual datasets. Performance optimization with gradient checkpointing. Practical exercises on AI text generation. Integration of prompt engineering in LoRA. Analysis of metrics: perplexity, BLEU score. Progress on the red thread project with real business cases. Common troubleshooting in LoRA fine-tuning.

Module 3Deployment and Optimization of LoRA Fine-Tuning

Fusion of LoRA adapters for fast inference. Deployment on servers with TorchServe or Hugging Face Spaces. Post-fine-tuning quantization for production. Fine-tuning evaluation on professional benchmarks. Security and bias in LoRA models. Production deployment of the red thread project. Exercises on multi-GPU scaling. Best practices for LoRA in business. Monitoring of deployed models. Closure with quiz and project defense.