Beginning Llamafile for Local Large Language Models (LLMs)
Learn to deploy and use local LLMs: Master Llamafile for private, efficient AI without cloud dependencies.
Learn to deploy and use local LLMs: Master Llamafile for private, efficient AI without cloud dependencies.
This course introduces learners to Llamafile, a powerful tool for running large language models (LLMs) locally. It focuses on practical skills for deploying and using LLMs without relying on cloud services, ensuring data privacy and reducing latency. The curriculum covers Llamafile's architecture, API usage, and integration with applications. Students will learn to create and customize Llamafiles, use the Mixtral model, and build portable binaries with Cosmopolitan. The course emphasizes hands-on experience, including running a Llamafile server and interacting with it using various tools.
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English
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What you'll learn
Learn how to serve large language models as production-ready web APIs using the llama.cpp framework
Understand the architecture and capabilities of the llama.cpp example server for text generation, tokenization, and embedding extraction
Gain hands-on experience in configuring and customizing the server using command line options and API parameters
Master the process of creating and using Llamafiles for local LLM deployment
Learn to build portable binaries with Cosmopolitan for cross-platform compatibility
Understand how to interact with the Llamafile API using tools like curl and Python
Skills you'll gain
This course includes:
29 Minutes PreRecorded video
4 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There is 1 module in this course
This course provides a comprehensive introduction to using Llamafile for deploying and running large language models (LLMs) locally. It covers the fundamentals of Llamafile, its architecture, and practical applications. Students will learn how to create and customize Llamafiles, use the Mixtral model, and build portable binaries with Cosmopolitan. The course emphasizes hands-on experience, including setting up a Llamafile server, interacting with its API, and integrating LLM capabilities into applications. Through a combination of video lectures, readings, quizzes, and practical exercises, learners will gain the skills to deploy powerful language models as scalable web APIs while maintaining data privacy and reducing latency.
Getting Started with Mozilla Llamafile
Module 1 · 3 Hours to complete
Fee Structure
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Instructors
Executive in Residence and Founder of Pragmatic AI Labs at Duke University
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the Master of Interdisciplinary Data Science (MIDS) program. He specializes in designing and teaching graduate-level courses on machine learning, MLOps, artificial intelligence, and data science, while also consulting on machine learning and cloud architecture for students and faculty. A recognized expert in the field, Gift is a Python Software Foundation Fellow and an AWS Machine Learning Hero, holding multiple AWS certifications, including AWS Certified Solutions Architect and AWS Certified Machine Learning Specialist. He has authored several influential books, such as Practical MLOps, Python for DevOps, and Pragmatic AI, and has published over 100 technical articles across various platforms, including Forbes and O'Reilly. His extensive industry experience includes roles as CTO and Chief Data Scientist for notable companies like Disney Feature Animation, Sony Imageworks, and AT&T, contributing to major films like Avatar and Spider-Man 3. Gift's work has generated millions in revenue through product development on a global scale. He actively consults startups on machine learning and cloud architecture while leading initiatives to enhance data science education.
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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