There are literally thousands of Large Language Models or LLMs available out there that can be used for a plethora of purposes. Hugging Face is the de-facto hub for language models, offering a huge collection where you can find and use almost any model you need. Choosing the right model can be an arduous task given models come in various shapes, sizes and configurations and each model is specialized at something different. So, when you approach Hugging Face in search of the right Model for your requirement, you have to know the art of this matchmaking.

In this course, we will learn how to navigate through the Hugging Face Hub for Models, matching their configurations to your needs. We will understand key characteristics of Models (LLMs), such as Size, Computational Requirements, Specializations, Licensing and so on. We will look into various families of Models and their specializations, performance and variants. We will also learn how to use various models from Hugging Face and Evaluate them based on your requirements. This course is designed for professionals deeply involved in the field of AI and machine learning, including Data Scientists, Machine Learning Engineers, AI Engineers, LLM RAG Application Developers, Software Developers, and IT Engineers. It targets individuals who are actively building or plan to build applications leveraging Large Language Models (LLMs) and seek to enhance their ability to select and utilize the most appropriate models for their specific needs. Participants should have a strong foundation in Python programming and a basic understanding of Large Language Models (LLMs) and their programmatic use, as the course will build on these concepts with practical coding exercises and advanced topics like model selection, comparison, and evaluation. By the end of this course, learners will have achieved four key objectives. They will master navigating the Hugging Face ecosystem, gaining proficiency in finding and understanding various models. They will also learn to effectively use these models, comparing them based on multiple factors and practical considerations. Additionally, the course will guide participants in testing and evaluating different models, enabling them to score and assess the results based on specific parameters. Ultimately, learners will be equipped to select the most suitable model for a given task, ensuring optimal performance in their applications.