Mastering LLMs Locally using Ollama | Hands-On

Hands-On Guide to Running, Fine-Tuning, and Integrating LLMs with Ollama

What you'll learn
- Fundamentals of LLMs & Ollama
- Using Ollama CLI & Desktop
- Run open LLMs like Gemma 3, Llama3
- Model Registry in Ollama for pushing customized models
- Token Count, Context length and Fine-tuning with your own datasets
- Ollama with REST API, Ollama-python Library, Integrating Ollama with Python & Streamlit
- Model Fine Tuning with Live Demonstration
- Building a local RAG application

Requirements
- Basic knowledge of Python
- Familiarity with AI/ML concepts and LLMs
- Interest in working with open-source tools for local AI deployment

Description
Large Language Models (LLMs) are at the core of today’s AI revolution, powering chatbots, automation systems, and intelligent applications. However, deploying and customizing them often feels complex and cloud-dependent. Ollama changes that by making it easy to run, manage, and fine-tune LLMs locally on your machine.

This course is designed for developers, AI enthusiasts, and professionals who want to master LLMs on their own hardware/laptop using Ollama. You’ll learn everything from setting up your environment to building custom AI models, fine-tuning them, and integrating them into real applications, all without relying on expensive cloud infrastructure.

What’s in this course?

We start with the fundamentals of LLMs and Ollama, explore their architecture, and understand how Ollama compares with tools like LangChain and Hugging Face. From there, you’ll set up Ollama across different operating systems, work with its CLI and desktop tools, and dive deep into model creation and management.

You will build practical projects, including:

Creating and configuring custom AI models usingModelfile

Integrating Ollama withPython, REST APIs, and Streamlit

Fine-tuning models with custom datasets (CSV/JSON)

Managing multiple versions of fine-tuned models

Building yourfirst local RAG (Retrieval-Augmented Generation) appwith Ollama

By the end, you’ll be fully equipped to deploy and run advanced LLM applications locally, giving you full control, privacy, and flexibility.

Special Note

This course emphasizeshands-on, practical learning. Every module includeslive demonstrationswith real-world troubleshooting, so you gain not just the theory but also the confidence to implement LLM solutions independently.

Course Structure

Lectures

Live Demonstrations

Course Contents

Introduction to LLMs and Ollama

Architecture of Ollama

Comparison - Ollama vs LangChain vs Hugging Face

Setting Up Ollama Environment

Commonly used Ollama Commands (CLI)

Understanding Model Configuration file (Modelfile)

Working with Models (Configuration, Registry, Tokens, Context length)

Ollama with Python (REST API, Python Library, Streamlit UI)

Model Fine-tuning and Version Management

Building Your First Local RAG App

All sections include hands-on demonstrations. Learners are encouraged to set up their own Ollama environments, follow along with the exercises, and reinforce their understanding through practical approach.

Who this course is for:
- AI/ML Engineers and Data Scientists
- AI/GenAI Enthusiasts looking to run models locally
- Tech Leads & Product Managers exploring LLM deployment options
- Developers, DevOps, and Cloud Engineers interested in open-source LLM workflows
More Info