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 LLaMA - Mastering Open-Source LLMs in the Enterprise training are: Solid knowledge of Python, basics of machine learning and PyTorch. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LLaMA - Mastering Open-Source LLMs in the Enterprise training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training LLaMA - Mastering Open-Source LLMs in t... | 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 LLaMA - Mastering Open-Source LLMs in 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 LLaMA - Mastering Open-Source LLMs in 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 LLaMA - Mastering Open-Source LLMs in 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 LLaMA architecture for professional applications

Fine-tune LLaMA models on enterprise-specific data

Deploy LLaMA with Hugging Face Transformers in production

Optimize LLaMA inference performance for scalability

Develop skills in integrating LLaMA into AI pipelines

Design sovereign AI solutions based on open-source LLaMA

Training program

Module 1LLaMA Fundamentals: Installation and Architecture (Hugging Face, Transformers, datasets)

Quick installation of the LLaMA environment with Hugging Face and PyTorch, exploration of the architecture of LLaMA 2 and 3 models, loading pre-trained weights for initial tests, practical exercises on tokenization and text generation, creation of a first functional LLaMA chatbot with output evaluation, trainer feedback on code and basic optimization.

Module 2Fine-tuning LLaMA: Adaptation to Business Data (LoRA, QLoRA, PEFT)

Preparation of enterprise datasets for LLaMA fine-tuning, implementation of efficient techniques like LoRA and QLoRA via the PEFT library, supervised training on concrete cases such as classification or assisted generation, monitoring with Weights & Biases, saving and merging of adapted models, collaborative exercises to validate performance on business metrics.

Module 3LLaMA Deployment: Servers and APIs (vLLM, FastAPI, Docker)

Containerization of LLaMA models with Docker for portability, high-performance deployment via vLLM and Ollama, creation of secure APIs with FastAPI for enterprise integration, load and latency testing under real conditions, configuration of CI/CD pipelines with GitHub Actions, production of a staging-ready LLaMA service with integrated monitoring.

Module 4Optimization and LLaMA Applications: RAG and AI Agents (LangChain, Security)

Implementation of RAG for LLaMA with vector databases FAISS and Pinecone, development of autonomous AI agents via LangChain, optimization through quantization and distillation for edge deployment, security audits against jailbreaks and biases, advanced use cases like enterprise document analysis, finalization of the ongoing project with cloud deployment and maintenance plan.