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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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Artificial Intelligence training in Glasgow in June 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Professional Training training in Memphis in October 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Artificial Intelligence training in Cardiff in May 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Professional Training training in Dallas in July 2026 with Learni. Certified, expert trainers, eligible for employer funding. Free quote.
Don't let this gap widen
Without Hugging Face mastery, 70% of NLP projects fail due to poor fine-tuning, wasting 6 months of R&D and 50k€ in useless resources.
Your competitors deploy AI chatbots in days, capturing 30% more customers via high-performing virtual assistants.
Risk staying stuck on obsolete models, losing 40% inference efficiency.
Avoid obsolescence: train to turn data into immediate revenue, scale your AI apps without recurring technical frustrations.
The Hugging Face Training - Master Open Source AI Transformers 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.
To ensure the quality of the Hugging Face Training - Master Open Source AI Transformers training, Learni provides the following 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.
The assessment of skills acquired during the Hugging Face Training - Master Open Source AI Transformers training is carried out through:
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.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Install the Transformers library, explore the Hugging Face Hub to download models and datasets, train your first BERT model on a concrete text classification exercise, create result visualizations with TensorBoard, produce an initial performance report that motivates you to dive deeper into generative AI.
Prepare your datasets with Tokenizers, fine-tune GPT-2 or DistilBERT on real cases like text generation, set up pipelines for NER and automatic translation, test on custom business data, generate precise metrics with Evaluate, deliver a functional prototype ready to integrate into your daily projects.
Optimize with Accelerate for multi-node GPUs, deploy your model on Hugging Face Spaces via Streamlit, integrate Inference Endpoints APIs, troubleshoot real error cases in groups, produce a portfolio of 3 deployed AI applications, leave with reusable templates to boost your productivity immediately.
Target audience
Data scientists, ML developers, AI engineers seeking to upskill on open-source models.
Prerequisites
Mastery of Python, basics in PyTorch or TensorFlow, knowledge of NLP and machine learning.
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