Harnessing Open Source LLMs and ChatGPT with Minimal Code from Coursera

This course offers a comprehensive guide to working with local large language models and ChatGPT, designed for technical professionals who want to explore these powerful tools without diving deep into coding. You’ll begin with the basics, including downloading necessary software, setting up local models, and interacting with them using a no-code approach. By the end of this section, you'll be proficient in managing and running LLMs locally, even if you’re new to the field.

As you advance, you’ll delve into more sophisticated techniques, such as streaming responses from local models and leveraging the OpenAI API. You’ll learn to set up your environment, handle API calls, and use Python to integrate different functionalities. Each module is crafted to build your skills progressively, ensuring you gain both practical experience and conceptual understanding of these technologies.

The final part of the course covers advanced integrations and function calling with ChatGPT, offering insights into the latest features and capabilities. You’ll explore various approaches to deploying models locally and on the web, using Python with minimal code. This course empowers you to harness the full potential of LLMs and ChatGPT for your projects, making advanced AI accessible and manageable for all skill levels.

This course is designed for developers, data scientists, and AI enthusiasts who want to leverage large language models and ChatGPT without extensive programming. Basic familiarity with Python is beneficial but not required, as the course starts with foundational topics and gradually introduces more advanced concepts.

What's inside

Syllabus

Setting Up and Exploring Local LLMs

In this module, we will cover the foundational steps to set up and work with local Large Language Models (LLMs). You’ll start by installing essential software, learning to use open-source models without coding, and setting up your development environment. By the end of this section, you’ll be able to run and explore local models using various tools and interfaces.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Focuses on leveraging large language models and ChatGPT without requiring extensive programming, making it accessible to professionals with varying levels of coding expertise

Covers setting up and managing local LLMs, which is valuable for professionals seeking to maintain data privacy and control over their AI applications

Explores advanced integrations and API usage with both open-source and OpenAI LLMs, enabling professionals to build more sophisticated and interactive applications

Teaches function calling with ChatGPT, offering insights into the latest features and capabilities, which is beneficial for staying current with AI technology

Requires basic familiarity with Python, which may be a barrier for some learners who are completely new to programming

Reviews summary

Practical llms with minimal code

According to learners, this course is a highly practical guide to working with open source LLMs and ChatGPT, particularly praised for its minimal code approach. Students found the content well explained and easy to follow, making it effective for getting started quickly with both local models and the ChatGPT API. The hands-on labs and demos are highlighted as particularly helpful. While the course delivers on its promise of low-code accessibility, a few students noted that a basic Python background is beneficial and some felt that certain parts were rushed or could use more depth. Overall, it is considered a solid and current course for those seeking practical LLM application without extensive programming.

Content is well explained

"Excellent course, very well explained and easy to follow."

"The explanations were straightforward."

"...the instructor explains complex topics clearly."

"The demos were clear and effective."

Emphasizes hands-on learning

"The hands-on labs were particularly helpful."

"Very practical and hands-on. I was able to get started with local LLMs and the ChatGPT API quickly after taking this course."

"Great demos and labs."

"This course is a gem for anyone who wants to use LLMs practically..."

Accessible even with minimal coding

"It delivers exactly what it promises - how to leverage LLMs without getting bogged down in complex coding."

"Perfect for getting up to speed with LLMs quickly. The 'minimal code' aspect was key for me..."

"...designed for technical professionals who want to explore these powerful tools without diving deep into coding."

"great for someone like me who isn't a deep coder"

Could use more advanced topics

"Some parts could use more depth."

"Decent course covering the basics... but I wish there were more advanced topics covered."

"As you advance, you’ll delve into more sophisticated techniques..."

Basic Python helpful, pace varies

"...I think having a bit more background in Python would be beneficial, despite the description saying it's not required."

"However, some parts felt a bit rushed..."

"...I felt lost occasionally without a stronger Python background."

"Basic familiarity with Python is beneficial but not required..."

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 Harnessing Open Source LLMs and ChatGPT with Minimal Code with these activities:

Review Python Fundamentals

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Reinforce your understanding of Python basics to better grasp the code examples used in the course.

Browse courses on Python

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Review basic syntax and data structures.
Practice writing simple Python scripts.
Familiarize yourself with common libraries.

Read 'The Art of Prompting: Unleashing the Power of Language Models'

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Learn effective prompting techniques to maximize the potential of LLMs and ChatGPT.

View Putting Knowledge to Work on Amazon

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Obtain a copy of 'The Art of Prompting'.
Read the chapters on prompt engineering techniques.
Experiment with different prompts on local LLMs.

Follow Tutorials on OpenAI API Usage

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Gain hands-on experience with the OpenAI API to understand how to integrate it with your projects.

Browse courses on OpenAI API

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Find tutorials on using the OpenAI API with Python.
Set up your OpenAI API key and environment.
Follow the tutorials to make API calls and process responses.

Four other activities

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Read 'Natural Language Processing with Transformers'

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Gain a deeper understanding of the transformer architecture that powers many LLMs.

View Natural Language Processing with Transformers,... on Amazon

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Obtain a copy of 'Natural Language Processing with Transformers'.
Read the chapters on transformer architecture and attention mechanisms.
Explore the code examples and experiment with different models.

Build a Simple Chatbot with a Local LLM

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Apply your knowledge by creating a chatbot that interacts with a local LLM.

Browse courses on LLMs

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Choose a local LLM to use for your chatbot.
Set up the environment and install necessary libraries.
Write code to handle user input and generate responses.
Test and refine your chatbot's performance.

Write a Blog Post on Your Experience with Local LLMs

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Share your insights and experiences with local LLMs to solidify your understanding and help others.

Browse courses on LLMs

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Reflect on your experience working with local LLMs.
Outline the key points you want to cover in your blog post.
Write and edit your blog post for clarity and accuracy.
Publish your blog post on a platform of your choice.

Contribute to an Open Source LLM Project

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Contribute to an open-source LLM project to gain practical experience and collaborate with other developers.

Browse courses on LLMs

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Find an open-source LLM project on GitHub or similar platform.
Review the project's documentation and contribution guidelines.
Identify a bug or feature to work on.
Submit a pull request with your changes.

Career center

Learners who complete Harnessing Open Source LLMs and ChatGPT with Minimal Code will develop knowledge and skills that may be useful to these careers:

AI Integrator

An AI Integrator helps organizations incorporate artificial intelligence into existing systems and workflows. This role involves setting up local large language models, which this course covers thoroughly. By learning how to manage and run LLMs locally, and integrating them with other tools, a student of this course may become proficient in the practical application of advanced AI technologies. The course specifically teaches how to use a no-code approach to interact with models. This is particularly helpful for those who need an accessible entry into AI integration. The course covers function calling with ChatGPT as well, which is vital for creating interactive solutions.

See salaries and explore the career path for AI Integrator

AI Solutions Architect

An AI Solutions Architect designs and implements artificial intelligence solutions. They need to be familiar with both local and cloud based models, and how to connect them into functional and scalable systems. This course helps build a foundation in this field, emphasizing practical experience with local LLMs and the OpenAI API without needing deep coding skills, including instruction on streaming responses and setting up an environment. These are critical skills for architects who need to choose appropriate integrations. The way the course introduces advanced integrations, such as function calling, also helps future architects to build sophisticated systems.

See salaries and explore the career path for AI Solutions Architect

AI Application Developer

An AI Application Developer builds software applications that incorporate artificial intelligence. This course may be useful as it teaches the basics of setting up local large language models and integrating them using API calls in Python. The modules on streaming responses from local models, and leveraging the OpenAI API are critical for developers in this field. A future AI Application Developer can benefit from the course's focus on practical skills that allow for creating sophisticated and interactive applications. This course helps extend the capabilities of language models, specifically through function calls.

See salaries and explore the career path for AI Application Developer

Automation Specialist

An Automation Specialist designs and implements systems that automate processes using technology, and this may include AI tools. This course helps explore how to leverage LLMs and ChatGPT without extensive programming. The course's focus on setting up local models and using APIs, can directly benefit an Automation Specialist. As the course helps to integrate different functionalities using Python, this empowers the Automation Specialist to expand their scope by automating processes that require AI-driven solutions. The course's modules on both local model deployment and web integration provide a comprehensive understanding, which prepares students for many opportunities in the field.

See salaries and explore the career path for Automation Specialist

AI Trainer

An AI Trainer prepares and refines machine learning models. This course helps in the understanding of how to manage and run LLMs, which is valuable for anyone working with large language models. The course's emphasis on no-code interaction and practical experience with local models, may be particularly useful, as often a trainer must test these models. Modules covering the OpenAI API and advanced function calling can further refine the skills of a trainer, giving them the ability to deploy and test advanced integrations. The course helps them grasp the nuances of these models, which in turn allows them to more effectively fine tune and improve them.

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Data Analyst

A Data Analyst interprets data and uses it to make recommendations for a business or organization. This course may be useful by teaching how to work with large language models and ChatGPT. The course's focus on using Python to interact with API calls and integrating different functionalities can prepare the Data Analyst to work with the growing intersection between data and AI. A Data Analyst will find that the course modules focusing on using local models and deployment on the web can offer valuable skills to add to their toolkit. The practical experience gained in working with these models helps in the analysis and interpretation of data.

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Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. This course may help a Machine Learning Engineer learn practical skills in setting up local LLMs, integrating using API calls, and deploying on the web. The course's focus on the OpenAI API and function calling can help the Machine Learning Engineer with the more advanced aspects of model deployment. The course helps build experience integrating large language models into applications, which can greatly support a Machine Learning Engineer's role. This experience may allow for better model management and deployment strategies.

See salaries and explore the career path for Machine Learning Engineer

Technology Consultant

A Technology Consultant advises organizations on how to best use technology to meet their goals, and this frequently includes AI. This course may be helpful as it provides a broad understanding of how to use large language models and ChatGPT. The course teaches both local model deployment and API integration, which will educate a Technology Consultant on available options in a growing field. The course modules on function-calling and web integration are also helpful, as they provide some of the latest features and capabilities. A Technology Consultant needs a broad understanding of technical options, and this course helps to provide just that.

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Software Developer

A Software Developer writes and tests software applications. This course may be useful for a Software Developer as it covers how to set up and interact with large language models. The course's focus on using Python to integrate with LLMs and the OpenAI API is directly relevant for a software developer. The modules on streaming responses, function calling, and web deployment are specifically useful for a developer looking to build applications incorporating advanced AI features. This course may offer both practical experience and the concepts for more sophisticated software development.

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Technical Project Manager

A Technical Project Manager oversees the planning, execution, and completion of technology-related projects. This course may be useful by providing an understanding of using LLMs and ChatGPT. A Technical Project Manager who is familiar with advanced AI technologies may be more effective. The course provides a non-coding approach to interacting with models, and provides a foundation by which to understand the technology. The course can help a Technical Project Manager guide projects and make informed decisions regarding the integration of AI tools. By being familiar with local deployment and API integration, the Technical Project Manager can estimate project timelines and resource requirements more accurately.

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Research Scientist

A Research Scientist conducts research to explore and develop new technologies. This course may be useful to a Research Scientist who is studying the applications of large language models, as it focuses on local LLMs and how they can be integrated into projects. The course offers practical experience in setting up, managing, and deploying these models. The focus on function calling may also help a researcher further investigate model capabilities. The course offers a practical understanding, which complements the theoretical work of a Research Scientist in artificial intelligence. An advanced degree such as a master's or a PhD is typically required for this role.

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Product Manager

A Product Manager guides the development and success of a product. This course may be helpful for a Product Manager who wishes to understand the capabilities and limitations of large language models. The course's approach to using LLMs without extensive coding may be useful to understand the technology landscape. The course covers how models can be deployed and integrated, and this gives a Product Manager a foundation of knowledge that can inform product development. By understanding this technology, a Product Manager can make strategic product decisions.

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Business Analyst

A Business Analyst identifies business needs and proposes solutions, and emerging technologies like AI are frequently considered. This course may be be useful if a Business Analyst is curious about the capabilities and applications of large language models and desires a starting point. By learning to work with local models and the OpenAI API, a Business Analyst can better understand how advanced AI technologies can be applied to business problems and this course covers these topics. The course may help a Business Analyst to make better technology recommendations and to also create more informed strategies using this technology.

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Technical Writer

A Technical Writer creates documentation for technical products and processes, and they may need some familiarity with the technology they describe. Although this course does not focus on producing documentation, it helps someone learn about LLMs and how to deploy them. The course provides instruction on how to set up local models, use APIs, and integrate different functionalities, as well as how to use both Python and a no-code approach. This background knowledge may be helpful for anyone who may need to write about these technologies, or who wishes to understand better how they work. The course gives a practical foundation in a rapidly evolving field.

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Educator

An Educator teaches learners in various settings. This course may be useful for an Educator who wants to learn about the capabilities of large language models without a deep dive into coding. The course offers practical instruction in setting up and using LLMs locally and on the web. An Educator can use the knowledge from this course to integrate AI into classes or to also understand how AI can impact education. They may also incorporate the no-code approach taught in this course into their own educational practices. The course helps provide an understanding of how LLMs operate, and how to best utilize them.

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Harnessing Open Source LLMs and ChatGPT with Minimal Code

What's inside

Syllabus

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Reviews summary

Practical llms with minimal code

Activities

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