Run Local LLMs with Ollama

Our goal is to equip you with a solid understanding of how to work with LLMs using both no-code tools and Python programming, enabling you to adapt these skills to various models based on your specific needs and preferences.

Welcome Learners, Unlock the power of cutting-edge AI with Meta LLaMA 3 in this comprehensive beginner-to-pro course. Whether you're new to AI or looking to deepen your expertise, this course offers a step-by-step guide to mastering Meta’s advanced LLaMA 3 language model using Ollama, an intuitive platform that simplifies working with local LLMs.

You’ll start with the basics, understanding what LLaMA 3 is and how it differs from other AI models. Gradually, you'll dive into hands-on projects that guide you through setup, fine-tuning, and leveraging its capabilities for real-world applications. By the end of the course, you’ll confidently use LLaMA 3 with Ollama to build projects, solve problems, and stay at the forefront of AI innovation.

Who Is This Course For?This course is designed for:

Beginners eager to explore AI with no prior experience.
Tech enthusiasts who want to understand and use advanced AI models.
Developers aiming to integrate AI into personal or professional projects.

What You will Learn ?

1. Introduction to AI , Neural Networks & LLM

1.1 Introduction1.2 What is AI - Artificial Intelligence1.3 AI Vs ML Vs DL1.4 What is a Neural Network?1.5 What are 1B/3B - Billions of Parameters1.6 What are the Model Benchmarks?1.7 What are Transformers?1.8 What is Embedding?1.9 What is Quantization?1.10 What is Context Length of LLM Model?

2. Introduction to Meta LLaMA

2.1 Title - Intro to Meta LLaMA2.2 Introduction to Meta LLaMA2.3 What is Meta LLaMA?2.4 History of LLaMA2.5 LLaMA 3.2 Model2.6 LLaMA 3.3 Model2.7 Differences between LLaMA and other LLMs like GPT2.8 How LLaMA processes text: tokens, embeddings, and attention mechanisms2.9 Artificial Analysis Quality Index2.10 Demo: Meta AI Chatbot

3. Deployment Strategies for Meta LLaMA Models

3.1 Title - Deployment Strategies for Meta LLaMA Models3.2 Introduction - Deployment Strategies3.3 What is Hugging Face?3.4 Demo: Requesting Access for LLaMA Models3.5 Demo: Running LLaMA Models with Hugging Face3.6 What is PyTorch?3.7 Demo: Running LLaMA Models with PyTorch3.8 Ollama3.9 Demo: Running LLaMA Models with Ollama3.10 Cloud Vendors (Azure)3.11 Demo: Running LLaMA Models with Azure

4. Introduction to Ollama

4.1 Title - Introduction to Ollama4.2 Introduction to Ollama4.3 What is Ollama?4.4 History of Ollama4.5 Benefits of Ollama4.6 Use-Cases Supported by Ollama

5. Setting up Ollama

5.1 Title - Setting up Ollama5.2 Introduction - Setup Ollama5.3 Walkthrough of Ollama Website5.4 System Requirements for Ollama5.5 Operating Systems Supported by Ollama5.6 Demo: Installing Ollama on MacOS5.7 Demo: Installing Ollama for Linux5.8 Demo: Installing Ollama via Docker

6. Ollama CLI

6.1 Title - Ollama CLI6.2 Introduction - Ollama CLI6.3 Ollama CLI Overview6.4 Demo: ollama help6.5 Demo: ollama pull6.6 Demo: ollama run6.7 Demo: ollama list6.8 Demo: ollama show6.9 Demo: ollama ps6.10 Demo: ollama cp6.11 Demo: ollama rm

7. Building Your Custom Model with Ollama

7.1 Title - Building Your Custom Model with Ollama7.2 Introduction - Your Own Custom Model7.3 What is a Model File?7.4 Demo: Understanding the Contents of a Model File7.5 Demo: Create Your Custom Model7.6 Demo: User Interaction7.7 Demo: Create Custom Model using GGUF File

8. OpenWebUI

8.1 Title - OpenWebUI8.2 Introduction8.3 What is OpenWebUI?8.4 Demo: Download Docker Desktop8.5 Demo: Run Docker Command to Install OpenWebUI8.6 Demo: Open the Web Browser & Use Chatbot

9. Using Various IDEs

9.1 Title - Using Various IDEs9.2 Introduction - Ollama with Various IDEs9.3 Setup Ollama with Jupyter Notebook9.4 Setup Ollama with Visual Studio Code9.5 Demo: Run a Sample Python Code9.6 Setup Ollama with Google Colab9.7 Demo: Run a Sample Python Code in Colab

10. Simple Python Codes in Ollama

10.1 Title - Simple Python Codes in Ollama10.2 Introduction - Simple Python Codes10.3 Demo: Setup Environment with GitHub Copilot10.4 Demo: Using ollama.generate10.5 Demo: Printing Required Artifacts10.6 Demo: Using ChatOllama10.7 Demo: Show Streaming with Ollama10.8 Demo: Ollama with a Custom Client10.9 Demo: Create Embedding in Ollama

11. Ollama & Multimodality

11.1 Title - Ollama & Multimodality11.2 Introduction - Multimodal Models11.3 What is Meta LLaMA 3.2 Vision Model?11.4 Demo: Analyze an Image Using Ollama CLI

12. LangChain with Ollama & LLaMA

12.1 Title - LangChain with Ollama & LLaMA12.2 Introduction - Ollama & LangChain12.3 What is LangChain?12.4 Ollama with LangChain - ChatOllama12.5 Demo: Setup Environment for LangChain Work12.6 Demo: A Simple Python Code with Ollama & LangChain12.7 Demo: Show the Chaining Concept in LangChain12.8 Demo: Increase the Level of Chaining, Convert Output to String

13. Ollama & OpenAI Compatibility

13.1 Title - Ollama & OpenAI Compatibility13.2 Introduction - Ollama & OpenAI Compatibility13.3 What is OpenAI?13.4 What is the Ollama & OpenAI Compatibility?13.5 Demo: How to Get the Same Code Working for Ollama

14. Getting Structured Outputs

14.1 Title - Getting Structured Outputs14.2 Introduction to Structured Outputs14.3 What are Structured Outputs with Ollama?14.4 Demo: Python Code for Structured Output14.5 Demo: Python Code to Get Objects in JSON Format from an Image

15. Tools in LLaMA & Ollama

15.1 Title - Tools in LLaMA & Ollama15.2 Introduction to Tools15.3 What are Tools in Ollama?15.4 Demo: Understand the Workflow15.5 Demo: Create an API Key in OpenWeatherMap15.6 Demo: Using Tools and Function Calling

What's inside

Learning objectives

Basics of artificial intelligence
What is deep learning ?
What is a neural network
Llm fundamentals
Intro to meta llama 3.2/3.3
Llama vs gpt
How llama processes text : tokens / attention mechanism
Deployment strategies - huggingface , pytorch, ollama, azure with demos
Deep dive into ollama
Installation of ollama on linux / mac / docker mode
Demos on ollama cli commands

Building first custom model with ollama & llama
Openwebui - the gui for ollama models
Demos- using ollama with vscode / colab / jupyter notebook
How to use simple python codes in ollama
Multimodal capabilities - analysing images
Langchain-ollama-llama
Openai compatibility
Structure outputs
Tools & ollama - function calling
Show more
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Basics of artificial intelligence
What is deep learning ?
What is a neural network
Llm fundamentals
Intro to meta llama 3.2/3.3
Llama vs gpt
How llama processes text : tokens / attention mechanism
Deployment strategies - huggingface , pytorch, ollama, azure with demos
Deep dive into ollama
Installation of ollama on linux / mac / docker mode
Demos on ollama cli commands
Building first custom model with ollama & llama
Openwebui - the gui for ollama models
Demos- using ollama with vscode / colab / jupyter notebook
How to use simple python codes in ollama
Multimodal capabilities - analysing images
Langchain-ollama-llama
Openai compatibility
Structure outputs
Tools & ollama - function calling
Show more
Show less

Syllabus

Large Language Models (LLM) Foundations (For Absolute Beginners)

Instruction

Title - LLM Foundations for Absolute Beginners

Introduction

When Ollama is Running Locally (on Your Computer):

Why Use Ngrok?

Google Colab can’t directly access your local machine.
Ngrok creates a secure public URL to expose your locally running Ollama instance to the internet.

Steps:

Run Ollama Locally:
- Start Ollama on your local machine (e.g., running at http://localhost:11434).
Install Ngrok and Start a Tunnel:
- Install Ngrok on your machine if not already installed.
- Run ngrok http 11434.
- Ngrok generates a public URL (e.g., https://xyz123.ngrok.io).
Use the Ngrok URL in Colab:
- In your Colab notebook, send API requests to the public Ngrok URL (e.g., https://xyz123.ngrok.io).

When Ollama is Running Remotely (on a Server):

Why Use Ngrok?

If the remote server doesn’t have a public IP or its ports are not accessible, Ngrok can expose it to the internet securely.

Steps:

Run Ollama on the Remote Server:
- Start Ollama on the remote server (e.g., running at http://localhost:11434).
Install and Run Ngrok on the Remote Server:
- SSH into the server.
- Run ngrok http 11434.
- Ngrok generates a public URL for the server (e.g., https://abc456.ngrok.io).
Use the Ngrok URL in Colab:
- In your Colab notebook, send API requests to the Ngrok URL (e.g., https://abc456.ngrok.io).

Career center

Learners who complete Run Local LLMs with Ollama: From No-Code to Python Code will develop knowledge and skills that may be useful to these careers:

Large Language Model Developer

A Large Language Model Developer specializes in creating, fine-tuning, and integrating advanced conversational AI systems into diverse applications. This role demands a strong understanding of LLM architecture and practical deployment skills, which is precisely what "Run Local LLMs with Ollama: From No-Code to Python Code" delivers. Learners directly engage with Meta Llama 3, mastering its local setup and customization via Ollama, along with essential Python programming for interaction and structured outputs. The course provides direct experience with building custom models, exploring multimodal capabilities, and integrating with frameworks like LangChain, directly preparing individuals to excel as a Large Language Model Developer. This specialized training is invaluable for building innovative solutions at the forefront of AI. An advanced degree may be helpful.

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Generative Artificial Intelligence Specialist

A Generative Artificial Intelligence Specialist focuses on designing, developing, and implementing applications powered by generative AI models like Large Language Models and multimodal systems. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," is an exceptionally strong fit for a Generative Artificial Intelligence Specialist, as its entire curriculum centers on mastering the practical aspects of working with LLMs. Learners gain deep expertise in running Meta Llama 3 locally using Ollama, building custom models, and leveraging Python for diverse generative tasks. The course covers multimodal capabilities, structured outputs, function calling, and integration with LangChain, all critical skills for creating sophisticated generative AI applications. This hands-on training ensures a solid foundation for innovation in this rapidly expanding field. An advanced degree may be helpful.

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Machine Learning Engineer

A Machine Learning Engineer is crucial for designing, building, and deploying intelligent systems that learn from data. This involves everything from model selection to integrating solutions into production environments. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," helps build a foundation in deploying and managing advanced large language models like Meta Llama 3 locally using powerful tools such as Ollama. The hands-on experience with Python coding, custom model building, and various deployment strategies including Hugging Face, PyTorch, and Azure, directly prepares learners for the practical challenges of a Machine Learning Engineer role. Understanding LLM fundamentals, multimodal capabilities, and framework integration with LangChain are vital for developing cutting-edge AI applications. An advanced degree is often beneficial for this role.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer focuses on developing and implementing AI-driven solutions across various platforms and applications. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," offers essential skills for this career path by providing deep exposure to large language models. Learners gain practical experience with Llama 3 and Ollama, mastering local deployment, customization, and integration using Python. The curriculum covers core AI concepts, deployment nuances with Docker and cloud vendors like Azure, and advanced topics such as multimodal AI and function calling. Such hands-on proficiency in building and running custom LLMs empowers an Artificial Intelligence Engineer to innovate and solve complex problems, making it a pivotal learning experience for advancing in the field. An advanced degree may be helpful.

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Natural Language Processing Engineer

A Natural Language Processing Engineer designs and implements systems that enable computers to understand, interpret, and generate human language. Proficiency with large language models is at the core of this field. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," helps build a foundation for aspiring Natural Language Processing Engineers by demystifying LLM fundamentals, including how models like Llama process text through tokens, embeddings, and attention mechanisms. The practical experience with local LLM deployment using Ollama and Python, alongside integration with LangChain, offers concrete skills for building and customizing language-based applications. Understanding structured outputs and multimodal capabilities further enhances a learner's ability to tackle diverse NLP challenges. An advanced degree is typically beneficial for this role.

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Deep Learning Engineer

A Deep Learning Engineer focuses on designing, training, and deploying neural network architectures, including advanced models like large language models. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," helps build a foundational understanding of deep learning concepts relevant to LLMs, such as neural networks, transformers, embeddings, and quantization. It provides practical, hands-on experience in deploying and customizing Meta Llama 3 locally using Ollama and Python code. This direct exposure to the practical aspects of working with deep learning models, including deployment strategies on various platforms, is instrumental for a Deep Learning Engineer aiming to specialize in the rapidly evolving field of generative AI. An advanced degree is often beneficial for this role.

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Software Developer Artificial Intelligence

A Software Developer Artificial Intelligence integrates AI capabilities into software applications, requiring both coding proficiency and an understanding of AI models. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," helps build a strong foundation for a Software Developer Artificial Intelligence by offering extensive practical experience with Python programming for large language models. Learners will master running and interacting with Meta Llama 3 locally via Ollama, integrating with various IDEs, and utilizing frameworks like LangChain. The focus on structured outputs, function calling, and OpenAI compatibility prepares developers to embed sophisticated AI features into their projects, making this course highly relevant for those looking to build innovative, AI-powered software solutions from the ground up.

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Solutions Architect Artificial Intelligence

A Solutions Architect Artificial Intelligence is responsible for designing robust, scalable, and efficient AI solutions that meet business needs. This requires a deep understanding of various AI technologies and deployment methodologies. "Run Local LLMs with Ollama: From No-Code to Python Code" provides crucial insights for a Solutions Architect Artificial Intelligence by covering practical deployment strategies for large language models. Learners gain hands-on experience with Ollama, Docker, and cloud platforms like Azure, understanding the benefits and use-cases of local LLM deployment. This knowledge is vital for making informed decisions about infrastructure, model selection, and integration points, ensuring that proposed AI architectures are technically sound and implementable. Understanding multimodal capabilities further broadens the scope of potential solutions. An advanced degree may be helpful.

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DevOps Engineer Machine Learning

A DevOps Engineer Machine Learning specializes in the operational aspects of machine learning models, ensuring their seamless deployment, monitoring, and maintenance in production environments. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," helps build a foundation for a DevOps Engineer Machine Learning by providing hands-on experience with the practical tooling necessary for LLM deployment. Learners delve into Ollama installation via Docker, Linux, and MacOS, and master its CLI commands for managing models. The course covers deployment strategies using cloud vendors like Azure and explores OpenWebUI for managing model interactions. These skills are directly applicable to MLOps practices, enabling efficient model versioning, continuous integration/continuous deployment pipelines, and robust management of local or edge AI deployments. An advanced degree is not typically required.

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Technical Consultant Artificial Intelligence

A Technical Consultant Artificial Intelligence advises clients on the strategic implementation and technical execution of AI solutions, often requiring expertise across various AI technologies. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," offers a robust skillset for a Technical Consultant Artificial Intelligence, providing practical understanding of large language model deployment and customization. Learners gain hands-on experience with Meta Llama 3 and Ollama, covering local setup, custom model creation, and integration with Python. The course's exploration of deployment strategies across Hugging Face, PyTorch, and Azure, alongside topics like function calling and structured outputs, enables consultants to recommend informed, actionable AI strategies tailored to specific client needs and existing infrastructures. An advanced degree may be helpful.

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Cloud Engineer Artificial Intelligence

A Cloud Engineer Artificial Intelligence is responsible for deploying, managing, and optimizing AI workloads and infrastructure within cloud environments. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," may be useful for a Cloud Engineer Artificial Intelligence as it provides specific training on deploying large language models using cloud vendors like Azure. While the primary focus is on local LLMs with Ollama, understanding how these models interact with cloud services, including remote deployment options and scaling considerations, is crucial. The course covers Ollama installation via Docker, which is a fundamental cloud deployment technology. This practical knowledge of AI model deployment, even locally, helps bridge the gap to enterprise-scale cloud AI solutions and infrastructure management.

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Prompt Engineer

A Prompt Engineer specializes in designing, testing, and refining prompts to maximize the effectiveness and steer the behavior of large language models for desired outputs. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," may be useful for a Prompt Engineer by deepening their understanding of how LLMs like Meta Llama 3 process text, including tokens, embeddings, and attention mechanisms. Practical experience with custom model building, achieving structured outputs (like JSON), and leveraging function calling provides insights into controlling model responses. While the course focuses on the technical running of LLMs, this foundational knowledge of LLM internals and output manipulation directly informs the crafting of more sophisticated and effective prompts, enabling a Prompt Engineer to achieve precise results.

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Artificial Intelligence Product Manager

An Artificial Intelligence Product Manager defines the vision, strategy, and roadmap for AI products, requiring a solid grasp of underlying AI technologies and their practical implications. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," may be useful for an Artificial Intelligence Product Manager as it provides practical, hands-on exposure to deploying and interacting with large language models, specifically Meta Llama 3 via Ollama. Understanding the nuances of local LLM deployment, custom model creation, multimodal capabilities, and integration with frameworks like LangChain allows product managers to assess technical feasibility, estimate development effort, and effectively communicate with engineering teams. This practical insight into LLM capabilities and limitations is invaluable for guiding the development of innovative AI products. An advanced degree may be helpful.

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Data Scientist

A Data Scientist analyzes complex datasets to extract insights, build predictive models, and inform strategic decisions, often involving machine learning techniques. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," may be useful for a Data Scientist looking to incorporate advanced generative AI capabilities into their toolkit. While the course's primary focus is on LLM deployment rather than statistical analysis, it provides a strong foundation in understanding how large language models function, their text processing mechanisms, and practical implementation using Python and Ollama. For data scientists interested in natural language processing, text generation, or leveraging LLMs for data augmentation and analysis, the skills in custom model building and integrating with frameworks like LangChain can be particularly relevant. An advanced degree is typically required for this role.

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Research Scientist, Artificial Intelligence

A Research Scientist Artificial Intelligence explores novel AI algorithms, develops theoretical frameworks, and conducts experiments to advance the state of the art in AI. This course, "Run Local LLMs with Ollama: From No-Code to Python Code," may be useful for a Research Scientist Artificial Intelligence as it provides a practical understanding of deploying and experimenting with large language models like Meta Llama 3 locally. While the course is more applied than theoretical, mastering LLM fundamentals, how models process text, and building custom models with Ollama offers a hands-on environment for testing hypotheses and evaluating model behaviors. The ability to quickly set up and run different open-source models provides a valuable experimental workbench, complementing deeper theoretical studies in AI research. An advanced degree (Master's or PhD) is typically required for this role.

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