MLOps for LLMs: Convert Phi3 to GGUF with LLaMA.cpp

Large Language Models (LLMs) offer immense potential, but deploying them efficiently, especially on local hardware, can be challenging. This course provides a clear path to understanding and implementing practical strategies for optimizing and running powerful small LLMs. By the end of this course, you will be able to convert and deploy optimized language models for local use, enabling you to build and experiment with LLM applications without extensive cloud resources. What you'll learn: * Understand the core principles of MLOps as applied to Large Language Models (LLMs). * Learn about various LLM model formats and the advantages of the GGUF format for efficient local inference. * Practice converting open-source LLMs, such as Phi3, into the optimized GGUF format. * Configure and utilize LLaMA.cpp to run GGUF models effectively on commodity hardware. * Apply fundamental MLOps concepts for managing and deploying small, efficient LLMs. This course begins by introducing foundational concepts of MLOps and LLM model architectures, then guides you through the practical steps of model conversion and local deployment. It provides a structured approach to make complex topics accessible. This course is designed for beginners interested in MLOps, LLMs, or local model deployment. No prior experience with LLM development or MLOps is required. Start building and deploying efficient LLM applications today.