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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.
10 spots per session maximum — 8 already taken
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30 free minutes with a training advisor — no commitment.
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Don't let this gap widen
Without mastery of NanoGPT for high-performance LLM development, teams waste up to 40% of GPU compute hours on inefficient training runs, costing enterprises $50,000+ per failed iteration.
75% of generative AI projects fail to scale due to suboptimal from-scratch implementations, leading to delayed product launches and lost revenue opportunities exceeding $1M annually for mid-sized firms.
Engineers risk obsolescence as competitors deploy 5x faster inference models, jeopardizing promotions and company market share.
Every delayed month without these skills amplifies vulnerabilities in NLP pipelines, exposing businesses to costly compliance breaches and innovation gaps.
The Complete Training: Mastering Karpathy's NanoGPT for Developing High-Performance LLM Models 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 Complete Training: Mastering Karpathy's NanoGPT for Developing High-Performance LLM Models 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 Complete Training: Mastering Karpathy's NanoGPT for Developing High-Performance LLM Models 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.
Universality of NanoGPT: origins, specificities, and role as a reference in the community. Installation of Python, PyTorch, CUDA/cuDNN for GPU users, Anaconda environment management. Hands-on with NanoGPT source code, exploration of official documentation, first runs.
Optimal collection of text corpora, legal and methodological limits (CC, OpenWebText, Wikipedia, custom files). Preprocessing, cleaning, sharding, BPE tokenization, adapted vocabulary. Initialization of a learning session, basic settings (epochs, batch, learning rate), overview of GPU/cloud usage.
Fine-tuning strategies: techniques, overfitting, specialized datasets. Monitoring learning (loss, perplexity), saving and managing checkpoints. Text generation, inference scripting, model export. Deployment: interface via API, integration into a web/Python application. Best practices, resource management, evolution perspectives.
Target audience
Developers, data scientists, and engineers wishing to deepen their skills in NLP and generative models.
Prerequisites
Mastery of the Python language, basics in machine learning, initial knowledge of deep learning, and understanding of Transformer-type architectures.
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