Run Local LLMs with Ollama: From No-Code to Python Code (Udemy.com)

In this course, Ollama Demystified: No-Code & Python Codes for AI Beginners, we have primarily used Meta Llama as the foundational Large Language Model (LLM). However, the techniques and methodologies covered throughout the course are not limited to Meta Llama alone. The same principles can be seamlessly applied to other open-source models such as DeepSeek, Qwen, and many more.

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 Introduction
1.2 What is AI - Artificial Intelligence
1.3 AI Vs ML Vs DL
1.4 What is a Neural Network?
1.5 What are 1B/3B - Billions of Parameters
1.6 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 LLaMA
2.2 Introduction to Meta LLaMA
2.3 What is Meta LLaMA?
2.4 History of LLaMA
2.5 LLaMA 3.2 Model
2.6 LLaMA 3.3 Model
2.7 Differences between LLaMA and other LLMs like GPT
2.8 How LLaMA processes text: tokens, embeddings, and attention mechanisms
2.9 Artificial Analysis Quality Index
2.10 Demo: Meta AI Chatbot

3. Deployment Strategies for Meta LLaMA Models

3.1 Title - Deployment Strategies for Meta LLaMA Models
3.2 Introduction - Deployment Strategies
3.3 What is Hugging Face?
3.4 Demo: Requesting Access for LLaMA Models
3.5 Demo: Running LLaMA Models with Hugging Face
3.6 What is PyTorch?
3.7 Demo: Running LLaMA Models with PyTorch
3.8 Ollama
3.9 Demo: Running LLaMA Models with Ollama
3.10 Cloud Vendors (Azure)
3.11 Demo: Running LLaMA Models with Azure

4. Introduction to Ollama

4.1 Title - Introduction to Ollama
4.2 Introduction to Ollama
4.3 What is Ollama?
4.4 History of Ollama
4.5 Benefits of Ollama
4.6 Use-Cases Supported by Ollama

5. Setting up Ollama

5.1 Title - Setting up Ollama
5.2 Introduction - Setup Ollama
5.3 Walkthrough of Ollama Website
5.4 System Requirements for Ollama
5.5 Operating Systems Supported by Ollama
5.6 Demo: Installing Ollama on MacOS
5.7 Demo: Installing Ollama for Linux
5.8 Demo: Installing Ollama via Docker

6. Ollama CLI

6.1 Title - Ollama CLI
6.2 Introduction - Ollama CLI
6.3 Ollama CLI Overview
6.4 Demo: ollama help
6.5 Demo: ollama pull
6.6 Demo: ollama run
6.7 Demo: ollama list
6.8 Demo: ollama show
6.9 Demo: ollama ps
6.10 Demo: ollama cp
6.11 Demo: ollama rm

7. Building Your Custom Model with Ollama

7.1 Title - Building Your Custom Model with Ollama
7.2 Introduction - Your Own Custom Model
7.3 What is a Model File?
7.4 Demo: Understanding the Contents of a Model File
7.5 Demo: Create Your Custom Model
7.6 Demo: User Interaction
7.7 Demo: Create Custom Model using GGUF File

8. OpenWebUI

8.1 Title - OpenWebUI
8.2 Introduction
8.3 What is OpenWebUI?
8.4 Demo: Download Docker Desktop
8.5 Demo: Run Docker Command to Install OpenWebUI
8.6 Demo: Open the Web Browser & Use Chatbot

9. Using Various IDEs

9.1 Title - Using Various IDEs
9.2 Introduction - Ollama with Various IDEs
9.3 Setup Ollama with Jupyter Notebook
9.4 Setup Ollama with Visual Studio Code
9.5 Demo: Run a Sample Python Code
9.6 Setup Ollama with Google Colab
9.7 Demo: Run a Sample Python Code in Colab

10. Simple Python Codes in Ollama

10.1 Title - Simple Python Codes in Ollama
10.2 Introduction - Simple Python Codes
10.3 Demo: Setup Environment with GitHub Copilot
10.4 Demo: Using ollama.generate
10.5 Demo: Printing Required Artifacts
10.6 Demo: Using ChatOllama
10.7 Demo: Show Streaming with Ollama
10.8 Demo: Ollama with a Custom Client
10.9 Demo: Create Embedding in Ollama

11. Ollama & Multimodality

11.1 Title - Ollama & Multimodality
11.2 Introduction - Multimodal Models
11.3 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 & LLaMA
12.2 Introduction - Ollama & LangChain
12.3 What is LangChain?
12.4 Ollama with LangChain - ChatOllama
12.5 Demo: Setup Environment for LangChain Work
12.6 Demo: A Simple Python Code with Ollama & LangChain
12.7 Demo: Show the Chaining Concept in LangChain
12.8 Demo: Increase the Level of Chaining, Convert Output to String

13. Ollama & OpenAI Compatibility

13.1 Title - Ollama & OpenAI Compatibility
13.2 Introduction - Ollama & OpenAI Compatibility
13.3 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 Outputs
14.2 Introduction to Structured Outputs
14.3 What are Structured Outputs with Ollama?
14.4 Demo: Python Code for Structured Output
14.5 Demo: Python Code to Get Objects in JSON Format from an Image

15. Tools in LLaMA & Ollama