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 Hugging Face Transformers - Deploying Advanced AI Models 2026 training are: Expertise in Python, PyTorch/TensorFlow, advanced NLP, and model fine-tuning. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Hugging Face Transformers - Deploying Advanced AI Models 2026 training is available in distanciel. The format can be adapted to your needs (inter, intra, custom).

Training Hugging Face Transformers - Deploying A... | 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 Hugging Face Transformers - Deploying Advanced AI Models 2026 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 Hugging Face Transformers - Deploying Advanced AI Models 2026 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 Hugging Face Transformers - Deploying Advanced AI Models 2026 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 Hugging Face Transformers architectures for professional AI projects

Develop optimized fine-tuning pipelines in a business environment

Implement scalable deployment of certified Transformers models

Design multimodal applications with Hugging Face 2026

Optimize AI performance to reduce inference costs

Integrate Transformers into secure ML DevOps workflows

Acquire expert skills to transform your business processes

Training program

Module 1Advanced Hugging Face Transformers Architectures: Dissection and Customization (PyTorch, Tokenizers)

Dive into the cores of Transformers BERT, GPT, and T5 via the Hugging Face library, analyze multi-head attention mechanisms and positional encodings, configure advanced tokenizers for rare languages, perform model customization exercises on business datasets, produce a first fine-tuned model with precise metrics, and validate performance via integrated benchmarks for immediate production mastery.

Module 2Expert Fine-Tuning of Hugging Face Transformers: Optimization and Transfer Learning (Trainer API, PEFT)

Master the Trainer API to accelerate fine-tuning of large models like Llama or Mistral, integrate PEFT techniques to reduce GPU consumption by 80%, apply transfer learning to real business semantic analysis cases, generate augmented datasets with Hugging Face pipelines, evaluate using ROUGE, BLEU, and perplexity, and deploy a customized model ready for business integration with optimized source code.

Module 3Production Deployment of Hugging Face Transformers: Scaling and Inference (Optimum, Docker, ONNX)

Set up scalable deployments with Optimum to export to ONNX and TensorRT, containerize via Docker and orchestrate with Kubernetes, test real-time inference on FastAPI, optimize latency for critical business applications, simulate massive loads with Locust, produce CI/CD pipelines for automatic updates, and secure models against adversarial attacks for certified robustness in 2026.

Module 4Multimodal Hugging Face Transformers Applications: Vision-Language and Audio (CLIP, Whisper, BLIP)

Explore multimodal models like CLIP and BLIP to fuse text and image, integrate Whisper for expert audio transcription, develop advanced RAG agents with hybrid retrieval, apply to real e-commerce or customer support projects, generate multimodal outputs via unified pipelines, measure cross-modal efficiency with FID and CLIPScore, and create an end-to-end prototype demonstrating immediate business value for your teams.

Module 5Best Practices and 2026 Innovations in Hugging Face Transformers: Security, Ethics, and Scaling (Guardrails, Agents)

Adopt ethical guardrails for aligned models, implement autonomous agents with LangChain and Transformers Agents, scale on GPU clusters with Accelerate, analyze biases using Hugging Face tools, prepare for 2026 updates like MoE and advanced quantization, review your red thread project with expert audit, and obtain a certificate validating your professional skills to boost your enterprise AI career.

Evaluation method

Expert multiple-choice quiz to validate learning outcomes at the end of the training
Continuous assessment through practical exercises and benchmarks
Defense of the multimodal red thread project in front of the trainer