Llm Crash Course: Run Models Locally. Master Llm Engineering

Explore LLM Engineering hands-on. Run and build GenAI apps locally, offline, and without any cloud or subscription.

What you'll learn

Set up and run open-source LLMs locally on Windows, macOS, or Linux with no cloud, no API keys, and zero recurring costs.

Install and configure tools like Python, Poetry, and model runtimes to deploy and manage LLMs in a fully offline environment

Use Python to send prompts and receive responses from local LLMs, simulating real conversations through structured message flows.

Understand LLM roles, token limits, context windows, streaming responses, and prompt design for better model control.

Build LLM tools like a customer support agent using function calling and structured responses.

Create a simple Retrieval-Augmented Generation (RAG) app to enhance your LLM with external data for better context-aware outputs.

Requirements

Knowledge of Python programming — you should know how to install python, write, debug and run Python scripts.

Familiarity with the command line or terminal — you’ll use basic commands during setup and testing.

A computer running Windows, macOS, or Linux — the course includes platform-specific setup instructions.

At least 8GB of RAM (16GB recommended) — running language models locally requires a reasonable amount of memory.

A stable internet connection for the initial setup only — after that, everything runs 100% offline.

No prior experience with LLMs, AI, or machine learning is required — all key concepts are explained with hands-on examples.

Description

Tired of relying on cloud-based AI tools that require subscriptions, API keys, and constant internet access to run LLMs? Why hold your LLM Enggineering journey?In this hands-on, fast-track course, you'll learn how to run powerful open-source language models locally on your own machine — 100% offline, private, and free forever. No recurring costs. No third-party services. Just you, your laptop, and your own AI environment.I’ll guide you through the exact setup process step-by-step on Windows, macOS, and Linux. Then, I will go beyond just setup — you'll also explore real-world LLM features like tools, Retrieval-Augmented Generation (RAG), streaming responses, and how to interact with LLMs using Python scripts and prompts.Whether you're a student, developer, or tech pro, this course empowers you to take full control of your LLM learning and workflows — without vendor lock-in or cloud dependency.What You’ll Learn:Set up a local environment to run cutting-edge LLMs.Use simple command-line tools to download and manage models.Write Python scripts to interact with and prompt local models.Learn Prompt Engineering with handson coding examples and understand how it impacts llm applicationsExplore key LLM concepts like RAG using LangChain, tools(callable functions), streaming, embeddings, vector databases and prompt engineering.Build a privacy-first, reusable LLM setup for internal tools, research, or personal projects.Avoid API keys, subscriptions, and internet dependency — forever.Who This Course Is For:Engineers & developers familiar with Python basics.Students or professionals looking to learn LLMs in a private, offline environment.Organizations exploring internal AI tools without relying on external APIs.I skip the fluff and unnecessary theory — this is a practical, no-nonsense crash course for modern LLM engineering with a unique offline-first approach.By the end, you’ll not only be running models entirely on your computer, but also have learned practical LLM concepts that you can apply across real-world environments. Let’s get started!

Overview

Section 1: Setting Up The Model

Lecture 1 Introduction

Lecture 2 Hardware Requirement for Running LLM Model Offline

Lecture 3 Preview of the Offline Model Running on My Mac

Lecture 4 About the Tool for Running Model Locally

Lecture 5 Setup and Connecting to the Model On Windows

Lecture 6 Setup and Connecting to the Model On Ubuntu

Lecture 7 Setup and Connecting to the Model On Mac

Section 2: Mastering LLM Concepts

Lecture 8 Setup Validation - Windows, Mac, Unix

Lecture 9 About LLM Roles, Token and Context Window

Lecture 10 Setting up Env: Python and Poetry

Lecture 11 Your First LLM Call: Sending Prompts and Handling Responses

Lecture 12 Understanding Role Based Prompting with Code Examples

Lecture 13 Coding Conversations: User and Assistant Message Flow

Lecture 14 LLM Tools: Lets Build Build a Customer Support Agent

Lecture 15 RAG - Part 1 - Lets Understand Key Concepts

Lecture 16 RAG - Part 2 - Handson

Developers and engineers who want to run powerful open-source LLMs locally without relying on cloud services, APIs, or subscriptions.,Python programmers looking to integrate language models into scripts, tools, or real-world applications in a fully offline environment.,Students or tech enthusiasts eager to explore AI and LLMs through a hands-on, practical course — without deep ML or data science background.,Makers, hackers, and open-source contributors who want full control over their AI tools and workflows with privacy and portability in mind.,Professionals in organizations exploring internal LLM applications for automation, chatbots, and document handling with no data leaving their system.,Anyone looking to quickly get up and running with LLMs, tools, prompt engineering, RAG, and chat-like experiences — all in a focused, crash-course format.