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 - Deploying Powerful LLMs in the Enterprise training are: Advanced mastery of Python, PyTorch, Hugging Face transformers, and GPU cluster management. A preliminary interview validates your eligibility.

Is the training available in person?

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

Training LLaMA - Deploying Powerful LLMs in 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 LLaMA - Deploying Powerful 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 - Deploying Powerful 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 - Deploying Powerful 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 internal architecture of LLaMA to optimize professional models

Develop LLaMA fine-tuning pipelines tailored to enterprise needs

Implement optimized LLaMA inference on scalable infrastructures

Design secure LLaMA deployments in certified production environments

Optimize LLaMA performance to reduce computational costs

Integrate LLaMA into enterprise AI applications with RAG and agents

Acquire certifying skills in open source LLMs for an impactful resume

Training program

Module 1LLaMA Architecture: Dissecting Advanced Models (Llama 3, LoRA, Hugging Face Tools)

Dive into the detailed analysis of LLaMA 3 architectures and variants, exploration of multi-layer transformers with exercises on custom tokenizers, setting up GPU environments via Docker and Hugging Face Transformers, first inference tests on professional datasets, creation of an optimized baseline model for your enterprise use case, with immediate trainer feedback to accelerate your expert skills.

Module 2LLaMA Fine-tuning: Customizing for Enterprise Data (PEFT, QLoRA, Curated Datasets)

Implement advanced fine-tuning techniques with PEFT and QLoRA on LLaMA, prepare domain-specific datasets via automated curation and augmentation, supervised training on GPU clusters with wandb monitoring, quantitative evaluation via perplexity and custom benchmarks, generate your first deployable fine-tuned model, practical exercises on real enterprise cases for immediate and profitable mastery.

Module 3LLaMA Optimization: Accelerating Inference (Quantization, Distillation, FlashAttention)

Radically optimize LLaMA performance via 4-bit and 8-bit quantization with bitsandbytes, implement knowledge distillation for lightweight models, integrate FlashAttention and vLLM for reduced latency, comparative benchmarks on varied hardware, hyperparameter tuning for maximum production throughput, develop a scalable inference pipeline tested on your data, directly enhancing the efficiency of your professional AI projects.

Module 4LLaMA Deployment: Scaling in Production (Kubernetes, vLLM, Secure APIs)

Deploy LLaMA in enterprise environments with Kubernetes and Ray Serve, configure vLLM inference servers for high traffic, secure APIs via OAuth and monitor with Prometheus/Grafana, set up load balancing and auto-scaling, load testing on real enterprise scenarios, deliver a fully functional deployment with CI/CD via GitHub Actions, ready for immediate integration into your critical systems.

Module 5Advanced LLaMA Applications: RAG, Agents, and Security (LangChain, Guardrails)

Build autonomous AI agents based on LLaMA with LangChain and LlamaIndex, implement RAG for precise contextual responses, integrate guardrails against hallucinations and biases via NeMo Guardrails, advanced use cases like enterprise chatbots and document analysis, final evaluation of your red thread project with business metrics, consolidation of certifying skills to boost your professional AI career.