{"id":14283,"date":"2026-08-10T11:11:41","date_gmt":"2026-08-10T11:11:41","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/pluralsight-introduction-to-llama-2025-12\/"},"modified":"2026-08-10T11:11:41","modified_gmt":"2026-08-10T11:11:41","slug":"pluralsight-introduction-to-llama-2025-12","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/pluralsight-introduction-to-llama-2025-12\/","title":{"rendered":"Pluralsight \u2013 Introduction to Llama 2025-12"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Introduction to Llama. This course provides a hands-on look at the open-source Llama language models, how to deploy them on local infrastructure, and build AI applications with complete privacy. As the most popular large-scale open-source language model, Llama is the focus of this course, which aims to empower professionals to run Meta models on local hardware and solve the challenges of high cloud API costs and privacy concerns. The course begins with an overview of the history and evolution of the Llama family and key licensing considerations. It then moves into the practical part, showing how to run models like Llama 3.1 8B locally with tools like Ollama to leverage conventional GPUs, and integrate them into Python applications. The course also covers advanced concepts such as retrieval-augmented generation (RAG) systems, which allow a model to respond to specific documents without the need for retraining, and provides a comparative analysis of the fine-tuning approach and RAG. In the end, participants will gain the knowledge and skills necessary to implement autonomous and secure AI infrastructures.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n