Foundations of Local Large Language Models
Learn to run and interact with LLMs locally using powerful tools. Explore LLM deployment, RAG, and ethical considerations.
Course Cost
₹ 2,699
Beginner
Skill Level
22 Hours
Self-paced lessons
This course provides a comprehensive introduction to running Large Language Models (LLMs) locally. You'll learn to set up a local environment using powerful tools to run different LLMs and interact with them via web interfaces and APIs. The course covers LLMOps, production workflows, performance evaluation, and responsible AI deployment. You'll gain hands-on experience with tools like llamafile, Hugging Face Candle, and Mozilla llamafile, and explore techniques like Retrieval Augmented Generation (RAG). The curriculum also addresses ethical considerations and strategies for responsible generative AI implementation.
What you'll learn
Set up and manage local environments for running Large Language Models (LLMs)
Use tools like llamafile, Hugging Face Candle, and Mozilla llamafile for LLM deployment
Implement Retrieval Augmented Generation (RAG) techniques to improve LLM context and performance
Evaluate real-world performance of LLMs using methods like Elo ratings
Explore production LLM workflows using tools such as skypilot, Lorax, and Ludwig
Understand and apply strategies for responsible generative AI deployment
Gain practical experience in testing and deploying LLM applications locally and on the cloud
Skills you'll gain
This course includes:
2.5 Hours PreRecorded video
7 quizzes, 2 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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There are 3 modules in this course
This course offers a comprehensive exploration of running Large Language Models (LLMs) locally. Students will learn to set up and manage local environments for LLMs using advanced tools and techniques. The curriculum covers three main areas: Local LLMOps, Production Workflows and Performance of LLMs, and Responsible Generative AI. Participants will gain hands-on experience with tools like llamafile, Hugging Face Candle, and Mozilla llamafile, and learn techniques such as Retrieval Augmented Generation (RAG). The course also addresses ethical considerations and strategies for responsible AI deployment, providing a well-rounded understanding of local LLM implementation and management.
Local LLMOps
Module 1 · 9 Hours to complete
Production Workflows and Performance of LLMs
Module 2 · 11 Hours to complete
Responsible Generative AI
Module 3 · 2 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.




