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: Python basics, machine learning fundamentals, and Linux environment. 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… | 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 fundamentals of LLaMA to develop certified AI applications

Install and configure LLaMA in a secure professional environment

Implement effective prompts to generate intelligent content

Develop fine-tuning skills tailored to enterprise needs

Design optimized LLaMA inference pipelines for production

Evaluate LLaMA performance and integrate business metrics

Deploy scalable LLaMA solutions in a professional context

Training program

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

Immersive discovery of LLaMA architecture, its open source variants, and its advantages for the enterprise. Quick installation via Hugging Face Transformers and Ollama on a local machine. Creation of a first secure test environment. First text generation tests with 7B and 13B models. Practical exercises on concrete cases like automated report generation. Validation of setups with code review by the trainer for a solid foundation.

Module 2Prompting LLaMA: Advanced Techniques and Optimization (chain-of-thought, few-shot)

Exploration of prompting techniques to maximize LLaMA precision. Hands-on practice of chain-of-thought and few-shot learning on professional scenarios like customer data analysis. Use of tools like LangChain to structure interactions. Interactive exercises on code generation and intelligent summaries. Comparative prompt testing to identify best practices. Production of deliverables ready to integrate into your enterprise workflows, with a focus on reducing hallucinations.

Module 3Fine-tuning LLaMA: Personalized Adaptation (LoRA, Enterprise Datasets)

Introduction to efficient LLaMA fine-tuning with LoRA to minimize resources. Preparation of internal datasets tailored to your use cases like automated customer support. Use of PEFT and Accelerate libraries for fast training on standard GPUs. Practical fine-tuning exercises on the ongoing project. Evaluation of performance gains with metrics like perplexity and ROUGE. Generation of custom models ready for immediate professional production deployment.

Module 4Deploying LLaMA: Inference and Integration (Docker, FastAPI API)

Mastery of LLaMA deployment in production via Docker and optimized containers. Creation of REST APIs with FastAPI to expose models in the enterprise. Implementation of scalable inference pipelines with vLLM. Load testing and monitoring with Prometheus. Integration into real web applications via exercises on a complete business case. Finalization of the ongoing project with final optimizations. Delivery of deployable scripts and an evolution plan for your AI skills.