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 - Adapting LLMs for the Enterprise training are: Mastery of Python, PyTorch or TensorFlow, basics of transformers and LLM fine-tuning. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training LoRA Fine-Tuning - Adapting LLMs for the… | 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 - Adapting LLMs for the Enterprise 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 - Adapting LLMs for the Enterprise 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 - Adapting LLMs for the Enterprise 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 the principles of LoRA to efficiently fine-tune large language models

Develop skills in GPU resource optimization during professional fine-tuning

Implement LoRA on open-source LLMs for certifying enterprise use cases

Design scalable and reproducible LoRA fine-tuning pipelines

Optimize the performance of adapted models with quantitative evaluation

Deploy LoRA fine-tuned models in secure production environments

Integrate LoRA into enterprise workflows for personalized AI

Training program

Module 1LoRA Fundamentals: Principles and Environment Setup (PyTorch, Hugging Face)

Dive into the mechanisms of Low-Rank Adaptation to drastically reduce fine-tuning costs, install the environment with PEFT and Hugging Face Transformers, configure a first base model like Llama or Mistral, perform initial tests on enterprise datasets, produce your first functional LoRA adapter with practical exercises and personalized code review to validate professional skills.

Module 2LoRA Implementation: Fine-tuning on Custom Datasets (Tokenizers, Data Loaders)

Prepare and tokenize your specific business datasets, integrate optimized data loaders for LoRA, launch full fine-tuning on an LLM with advanced hyperparameters like rank and alpha, monitor metrics in real-time via Weights & Biases, apply regularization techniques to avoid overfitting, generate performance reports, and iterate on concrete enterprise cases for immediate and certifying results.

Module 3LoRA Optimization: Quantization and Multi-GPU (bitsandbytes, DeepSpeed)

Advance to optimization with 4-bit and 8-bit quantization via bitsandbytes, set up multi-GPU sessions to accelerate LoRA fine-tuning on large data volumes, integrate DeepSpeed for ZeRO-Offload, test on high-scale enterprise scenarios, measure time and memory gains, produce ultra-lightweight models ready for deployment with comparative benchmarks to demonstrate professional value.

Module 4LoRA Evaluation and Merging: Advanced Metrics and Model Fusion

Develop comprehensive evaluation scripts with ROUGE, BLEU, and perplexity on your fine-tuned LoRA models, conduct human and automated A/B tests, merge LoRA adapters into the base model using mergekit, optimize for fast inference, analyze biases and robustness on real enterprise cases, generate deliverables like interactive dashboards for certifying validation and smooth production integration.

Module 5LoRA Deployment: CI/CD Pipelines and Serving (vLLM, Docker, Kubernetes)

Containerize your fine-tuned LoRA models with Docker for maximum portability, deploy via Kubernetes or vLLM servers for high-performance inference, integrate CI/CD pipelines with GitHub Actions, secure API endpoints for enterprise use, simulate production loads with stress tests, finalize a complete red thread project with documentation and live deployment, ready for immediate and certifying implementation.