Duke University
Foundations of Local Large Language models
Duke University

Foundations of Local Large Language models

Taught in English


Gain insight into a topic and learn the fundamentals

Noah Gift
Alfredo Deza

Instructors: Noah Gift

Beginner level

Recommended experience

23 hours to complete
3 weeks at 7 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Local Large Language Models (LLMs)

    Tools for running LLMs locally like Llamafile

Details to know

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Recently updated!

March 2024


7 quizzes, 2 assignments

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There are 3 modules in this course

This week, you will learn mitigation strategies, evaluate task performance, and operationalize workflows by identifying risks in notebooks and deploying an LLM application.

What's included

21 videos16 readings3 quizzes1 assignment1 discussion prompt1 ungraded lab

This week, you will explore different types of generative AI applications, including API-based, embedded model, and multi-model systems. You'll learn the fundamentals of building robust applications using techniques like Retrieval Augmented Generation (RAG) to improve context. Through hands-on exercises, you'll gain experience evaluating real-world performance of large language models using Elo ratings coded in Python, Rust, R, and Julia. Then you'll explore production LLM workflows using tools like skypilot, Lorax, and Ludwig for fine-tuning models like Mistral-7b. Finally, you'll gain hands-on experience testing an application locally and deploying it on the cloud.

What's included

13 videos13 readings3 quizzes1 assignment4 ungraded labs

This week you will learn foundations of generative AI and responsible deployment strategies to benefit from the latest advancements while maintaining safety, accuracy, and oversight. By directly applying concepts through hands-on labs and peer discussions, you will gain practical experience putting AI into production.

What's included

7 videos4 readings1 quiz1 ungraded lab


Noah Gift
Duke University
40 Courses91,762 learners

Offered by

Duke University

Recommended if you're interested in Machine Learning

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