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Noah Gift
and
Alfredo Deza
By the end of this course, a learner will have a solid understanding of Large Language Models running locally. You'll be able to setup a local environment using powerful tooling to run different LLMs and interact with them both with a web interface as well as with APIs.
You will explore other tools and programming languages to interact with these LLMs and using LLMs via via Hugging Face Candle and Mozilla llamafile.
Register for this course and see more details by visiting:
OpenCourser.com/course/8urui7/foundations
What's inside
Syllabus
Local LLMOps
This week, you will learn mitigation strategies, evaluate task performance, and operationalize workflows by identifying risks in notebooks and deploying an LLM application.
Read more
Syllabus
Local LLMOps
This week, you will learn mitigation strategies, evaluate task performance, and operationalize workflows by identifying risks in notebooks and deploying an LLM application.
Read more
Production Workflows and Performance of LLMs
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.
Responsible Generative AI
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.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores Large Language Models (LLMs), which are currently being used in industry
Provides practical experience with setting up an LLM environment and interacting with LLMs using web interfaces and APIs
Covers responsible deployment strategies for generative AI, emphasizing safety, accuracy, and oversight
Offers hands-on labs and interactive materials to reinforce learning and provide practical experience
Taught by Noah Gift and Alfredo Deza, recognized experts in the field of LLMs
Requires learners to have some familiarity with Python and Jupyter Notebooks
Save this course
Save Foundations of Local Large Language models to your list so you can find it easily later:
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Foundations of Local Large Language models with these
activities:
Review core programming concepts
Show steps
Revisit the fundamentals of programming, including data structures, algorithms, and software design principles, to strengthen your foundation for understanding LLMs.
Browse courses on
Large Language Models
Show steps
-
Review core concepts using online resources or textbooks
-
Practice solving coding problems on platforms like LeetCode or HackerRank
Attend an LLM Workshop or Conference
Show steps
Enhance knowledge and network with experts by attending an LLM workshop or conference.
Browse courses on
LLMs
Show steps
-
Identify and register for an upcoming LLM workshop or conference.
-
Prepare by researching the topics and speakers.
-
Actively participate in sessions and take notes.
-
Engage with speakers and fellow attendees to exchange ideas.
Create LLM applications with APIs
Show steps
Gain hands-on experience by building projects using LLM APIs. This will solidify your understanding of how LLMs work and their practical applications.
Show steps
-
Choose a project idea that aligns with your interests
-
Research and select an appropriate LLM API
-
Develop your application using Python and the selected API
-
Test and iterate on your application to refine its functionality
Five other activities
Expand to see all activities and additional details
Show all eight activities
Hands-on LLM Workflow Exploration
Show steps
Solidify understanding of LLM workflows through guided tutorials, gaining hands-on experience.
Browse courses on
LLMOps
Show steps
-
Set up a local environment for LLM execution.
-
Experiment with different LLMs using API-based and embedded model approaches.
-
Apply techniques like RAG to enhance context in LLM responses.
-
Evaluate LLM performance using Elo ratings and other metrics.
-
Deploy an LLM application on the cloud.
Peer-led LLM Project Discussion
Show steps
Collaborate with peers to discuss LLM projects, sharing ideas and gaining diverse perspectives.
Browse courses on
LLMOps
Show steps
-
Form a peer study group with 2-3 classmates.
-
Select an LLM project or use case to discuss.
-
Research and prepare talking points on the chosen topic.
-
Meet virtually or in person to discuss the project.
-
Share ideas, ask questions, and provide constructive feedback.
LLM Prompt Engineering Exercises
Show steps
Strengthen prompt engineering skills through dedicated practice exercises.
Browse courses on
LLMs
Show steps
-
Craft prompts for different tasks, such as text generation, translation, and question answering.
-
Experiment with various prompt formats and techniques.
-
Evaluate the effectiveness of different prompts based on LLM responses.
-
Iterate and refine prompts to achieve optimal results.
LLM Blog Post or Article
Show steps
Deepen comprehension by creating a blog post or article that synthesizes LLM knowledge.
Browse courses on
LLMs
Show steps
-
Choose a specific aspect of LLMs to focus on.
-
Research and gather information from reputable sources.
-
Organize and structure the content logically.
-
Write the blog post or article in a clear and engaging style.
-
Publish and share your work for feedback.
LLM-Generated Creative Work
Show steps
Foster creativity by leveraging LLMs to generate a unique piece of writing, art, or music.
Browse courses on
LLMs
Show steps
-
Define the parameters and scope of the creative work.
-
Use LLMs to generate ideas, content, or artistic elements.
-
Refine and curate the LLM-generated output.
-
Combine and assemble the components into a cohesive creative work.
-
Present the final work and reflect on the process.
Review core programming concepts
Show steps
Revisit the fundamentals of programming, including data structures, algorithms, and software design principles, to strengthen your foundation for understanding LLMs.
Browse courses on
Large Language Models
Show steps
Show steps
Show steps
- Review core concepts using online resources or textbooks
- Practice solving coding problems on platforms like LeetCode or HackerRank
Attend an LLM Workshop or Conference
Show steps
Enhance knowledge and network with experts by attending an LLM workshop or conference.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Identify and register for an upcoming LLM workshop or conference.
- Prepare by researching the topics and speakers.
- Actively participate in sessions and take notes.
- Engage with speakers and fellow attendees to exchange ideas.
Create LLM applications with APIs
Show steps
Gain hands-on experience by building projects using LLM APIs. This will solidify your understanding of how LLMs work and their practical applications.
Show steps
Show steps
Show steps
- Choose a project idea that aligns with your interests
- Research and select an appropriate LLM API
- Develop your application using Python and the selected API
- Test and iterate on your application to refine its functionality
Five other activities
Expand to see all activities and additional details
Show all eight activities
Hands-on LLM Workflow Exploration
Show steps
Solidify understanding of LLM workflows through guided tutorials, gaining hands-on experience.
Browse courses on
LLMOps
Show steps
Show steps
Show steps
- Set up a local environment for LLM execution.
- Experiment with different LLMs using API-based and embedded model approaches.
- Apply techniques like RAG to enhance context in LLM responses.
- Evaluate LLM performance using Elo ratings and other metrics.
- Deploy an LLM application on the cloud.
Peer-led LLM Project Discussion
Show steps
Collaborate with peers to discuss LLM projects, sharing ideas and gaining diverse perspectives.
Browse courses on
LLMOps
Show steps
Show steps
Show steps
- Form a peer study group with 2-3 classmates.
- Select an LLM project or use case to discuss.
- Research and prepare talking points on the chosen topic.
- Meet virtually or in person to discuss the project.
- Share ideas, ask questions, and provide constructive feedback.
LLM Prompt Engineering Exercises
Show steps
Strengthen prompt engineering skills through dedicated practice exercises.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Craft prompts for different tasks, such as text generation, translation, and question answering.
- Experiment with various prompt formats and techniques.
- Evaluate the effectiveness of different prompts based on LLM responses.
- Iterate and refine prompts to achieve optimal results.
LLM Blog Post or Article
Show steps
Deepen comprehension by creating a blog post or article that synthesizes LLM knowledge.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Choose a specific aspect of LLMs to focus on.
- Research and gather information from reputable sources.
- Organize and structure the content logically.
- Write the blog post or article in a clear and engaging style.
- Publish and share your work for feedback.
LLM-Generated Creative Work
Show steps
Foster creativity by leveraging LLMs to generate a unique piece of writing, art, or music.
Browse courses on
LLMs
Show steps
Show steps
Show steps
- Define the parameters and scope of the creative work.
- Use LLMs to generate ideas, content, or artistic elements.
- Refine and curate the LLM-generated output.
- Combine and assemble the components into a cohesive creative work.
- Present the final work and reflect on the process.
Career center
Learners who complete Foundations of Local Large Language models will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
The coursework on local large language models can be immediately applied to the work of a Machine Learning Engineer. Machine Learning Engineers work with, design, and implement ML models that are applied to a variety of tasks across many industries. Building off of the knowledge in this course will increase the effectiveness of your models, their deployment, and their evaluation.
Data Scientist
Data Scientist
Data Scientists collect, analyze, and interpret large amounts of data. They are highly skilled professionals who work in a variety of fields, including finance, healthcare, marketing, and retail. The knowledge gained in this course will help you build a strong foundation for your career as a Data Scientist, as you will learn how to build, train, and evaluate machine learning models using large language models.
Software Engineer
Software Engineer
A Software Engineer with a focus on AI can position themselves well by taking this course, bringing their abilities to the next level. Software Engineers research, design, and develop software applications. They play a key role in ensuring that these applications are efficient, reliable, and user-friendly. By learning about large language models, you will be able to develop more powerful and innovative applications.
Research Scientist
Research Scientist
Research Scientists are responsible for conducting research in a variety of scientific fields and developing new theories. They use their knowledge and expertise to solve problems and create new knowledge. The coursework in this course on large language models will help you develop the skills you need to be successful as a Research Scientist, as you will learn how to design and conduct research studies.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. They work with engineers, designers, and marketing teams to ensure that products are successful. By completing this course, you will learn about the latest advancements in large language models and how to use them to build successful products. This knowledge will be invaluable to you in your career as a Product Manager.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. They play a key role in making investment decisions. The coursework in this course on large language models will help you develop the skills you need to be successful as a Quantitative Analyst, as you will learn how to use these models to analyze data and make predictions.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data. They use their findings to identify trends and patterns which can then be used to make informed decisions. This course will help you build a strong foundation for your career as a Data Analyst, as you will learn how to use large language models to analyze data and extract insights.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers research, design, and develop artificial intelligence systems. They work on a variety of projects, including self-driving cars, facial recognition software, and natural language processing. By completing this course, you will learn about the latest advancements in large language models and how to use them to build AI systems. This knowledge will be invaluable to you in your career as an Artificial Intelligence Engineer.
Machine Learning Scientist
Machine Learning Scientist
Machine Learning Scientists research and develop new machine learning algorithms and techniques. They work on a variety of projects, including developing new ways to diagnose diseases using medical data, creating new financial trading strategies, and developing self-driving cars. By completing this course, you will learn about the latest advancements in large language models and how to use them to develop new machine learning algorithms. This knowledge will be invaluable to you in your career as a Machine Learning Scientist.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement natural language processing systems. They work on a variety of projects, including machine translation, chatbots, and text summarization. By completing this course, you will learn about the latest advancements in large language models and how to use them to develop natural language processing systems. This knowledge will be invaluable to you in your career as a Natural Language Processing Engineer.
Business Analyst
Business Analyst
Business Analysts help businesses improve their performance by analyzing their operations and recommending changes. They work with a variety of stakeholders, including executives, managers, and employees. By completing this course, you will learn about the latest advancements in large language models and how to use them to analyze data and make recommendations. This knowledge will be invaluable to you in your career as a Business Analyst.
Data Architect
Data Architect
Data Architects design and implement data management systems. They work with a variety of stakeholders, including database administrators, data scientists, and business analysts. By completing this course, you will learn about the latest advancements in large language models and how to use them to design and implement data management systems. This knowledge will be invaluable to you in your career as a Data Architect.
Software Architect
Software Architect
Software Architects design and implement software systems. They work with a variety of stakeholders, including software engineers, project managers, and business analysts. By completing this course, you will learn about the latest advancements in large language models and how to use them to design and implement software systems. This knowledge will be invaluable to you in your career as a Software Architect.
Systems Engineer
Systems Engineer
Systems Engineers design and implement complex systems. They work with a variety of stakeholders, including engineers, scientists, and business analysts. By completing this course, you will learn about the latest advancements in large language models and how to use them to design and implement complex systems. This knowledge will be invaluable to you in your career as a Systems Engineer.
Technical Writer
Technical Writer
Technical Writers create documentation for technical products. They work with a variety of stakeholders, including engineers, scientists, and business analysts. By completing this course, you will learn about the latest advancements in large language models and how to use them to create documentation for technical products. This knowledge will be invaluable to you in your career as a Technical Writer.
For more career information including salaries, visit:
OpenCourser.com/course/8urui7/foundations
Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Foundations of Local Large Language models.
Provides a comprehensive overview of deep learning techniques for NLP, including Transformers and LLMs. It valuable resource for anyone who wants to learn more about the theoretical foundations of LLMs.
Provides a comprehensive overview of deep learning with Python, including the use of LLMs. It valuable resource for anyone who wants to learn more about the practical applications of LLMs.
Provides a comprehensive overview of NLP in action, including the use of LLMs. It valuable resource for anyone who wants to learn more about the practical applications of LLMs.
Provides a comprehensive overview of NLP with Python, including the use of LLMs. It valuable resource for anyone who wants to learn more about the practical applications of LLMs.
Provides a practical introduction to deep learning with fastai and PyTorch, including the use of LLMs. It valuable resource for anyone who wants to learn more about the practical applications of LLMs.
Provides a concise overview of machine learning, including the use of LLMs. It valuable resource for anyone who wants to learn more about the theoretical foundations of LLMs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8urui7/foundations
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Effort
5 hours a work a week of study for a total of 15 hours
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Language
English
Good to know
Explores Large Language Models (LLMs), which are currently being used in industry
Provides practical experience with setting up an LLM environment and interacting with LLMs using web interfaces and APIs
Covers responsible deployment strategies for generative AI, emphasizing safety, accuracy, and oversight
Offers hands-on labs and interactive materials to reinforce learning and provide practical experience
Taught by Noah Gift and Alfredo Deza, recognized experts in the field of LLMs
Requires learners to have some familiarity with Python and Jupyter Notebooks
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