Llm Crash Course: Run Models Locally. Master Llm Engineering

Llm Crash Course: Run Models Locally. Master Llm Engineering
Published 6/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

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.
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

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 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

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.