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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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The Complete Training: Mastering Meta LLaMA for Text Generation and Advanced AI 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 Meta LLaMA for Text Generation and Advanced AI 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 Meta LLaMA for Text Generation and Advanced AI training is carried out through:
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Introduction to large-scale language models (LLM), history of LLaMA and positioning in the AI ecosystem, presentation of main use cases, hands-on Python for AI, review of the software environment (CUDA, PyTorch, dependencies), quick installation and configuration of Meta LLaMA on local and cloud.
Analysis of LLaMA architecture, discovery of different models and parameters, in-depth study of the tokenizer, simple text generation, introduction to prompt engineering, preparation of custom datasets, fine-tuning procedures and performance evaluation in Python, memory management and GPU optimization.
Deployment of a fine-tuned LLaMA model in a web API (FastAPI, Flask), integration with business applications (chatbot, summarization engine, assisted content creation), bias management and output control, security, data governance and legal limits, best practices guide and anticipation of field developments.
Target audience
Developers, data scientists, AI engineers, R&D teams wishing to exploit and integrate Meta LLaMA for text generation and generative AI
Prerequisites
Basic Python programming skills, elementary knowledge of neural networks and Linux environments
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