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.

What is the duration of the Training LoRA Fine-Tuning - Personalizing AI Models training?

The Training LoRA Fine-Tuning - Personalizing AI Models training takes place over 4 journées. The program is structured in progressive modules for optimal skill development.

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 - Personalizing AI Models training are: Python basics, machine learning fundamentals, PyTorch or Hugging Face Transformers. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training LoRA Fine-Tuning - Personalizing AI Models | 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 - Personalizing 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 - Personalizing 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 - Personalizing 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 access terms and deadlines

Training objectives

Master LoRA principles for fine-tuning professional AI models

Set up the LoRA training environment with PEFT and Hugging Face tools

Develop skills in preparing datasets for enterprise fine-tuning

Implement LoRA fine-tuning on LLMs and diffusion models

Optimize LoRA adapter performance for production

Evaluate and deploy certified LoRA models in professional contexts

Training program

Module 1LoRA Fundamentals: Principles and Advantages for Cost-Effective Fine-Tuning (Theory, Full Tuning Comparison, PEFT)

Discover the basics of LoRA fine-tuning through an engaging pedagogical immersion, explore low-rank adaptation mechanisms that reduce trainable parameters by 99% compared to full fine-tuning, concrete comparisons with examples on LLMs like Llama, guided exercises to identify enterprise use cases, creation of custom diagrams, and discussions on GPU resource savings, all to build solid and motivating foundations for certified AI projects.

Module 2LoRA Environment: Installation and Dataset Preparation (Hugging Face, PEFT, Tokenizers)

Install your complete LoRA stack live with the trainer, hands-on with the PEFT and Hugging Face Transformers libraries for quick and secure setup, prepare custom datasets tailored to your professional needs such as text for chatbots or images for generation, automated cleaning and tokenization on real enterprise cases, initial tests loading base models like Mistral, collaborative practical exercises with immediate feedback to build autonomy and confidence by the second day.

Module 3LoRA Practice: Hands-On Fine-Tuning on LLMs (Training, Hyperparameters, Monitoring)

Dive into active LoRA fine-tuning on a customizable ongoing project, select and adapt an open-source LLM to your business data using ready-to-use scripts, tune optimal hyperparameters like rank and alpha for fast results, real-time monitoring with Weights & Biases, group troubleshooting of common bugs on enterprise scenarios, produce a functional fine-tuned model ready for immediate testing, with a focus on business value such as 30% precision improvement for internal AI assistants.

Module 4LoRA Deployment: Optimization and Production Integration (Merge Adapters, Serving, Evaluation)

Finalize your LoRA model by merging adapters for smooth and lightweight deployment, optimize for fast inference with quantization and ONNX, quantitative evaluation tests using metrics like perplexity or BLEU on professional benchmarks, integrate into an enterprise pipeline via Docker and FastAPI, simulate production serving with real workloads, deliver complete assets including source code and certification report, for immediate professional application and visible ROI.