Llm 101 - The Only Llm Foundation Course You'Ll Need In 2025

Master the Fundamentals of Large Language Models, From Basics to Practical Implementation, with REAL WORLD UNDERSTANDING

What you'll learn

EVERYTHING YOU NEED TO KNOW ABOUT LLMS, starting with: The position of LLMs within the broader field of Artificial Intelligence.

The history and evolution of LLMs from early Natural Language Processing (NLP) techniques to advanced Deep Learning models.

The significance of large datasets in pretraining LLMs and why these models are termed "large."

Key components of LLMs, including tokenization, transformer architecture, and the attention mechanism.

Different types of LLM architectures, such as GPT, BERT, and PaLM.

How to handle model weights, understand their creation, and storage formats.

Detailed insights into the training data, bias, and fairness issues, and popular datasets used in LLMs.

Practical guidance on running LLMs offline, including hardware requirements and suitable models.

An in-depth analysis of KoboldCPP and other relevant tools.

Comprehensive understanding of tuning and sampling parameters and their impact on LLM performance.

The final recap of key concepts, addressing misconceptions, and exploring the future opportunities and threats of LLMs.

Requirements

No Prior Coding Experience Required:

An interest in natural language processing and how machines understand and generate human language.

A computer with a stable internet connection to access course materials and complete online projects.

Optional: A basic understanding of artificial intelligence and machine learning concepts will be helpful but is not mandatory. The course will cover necessary foundational topics.

Optional: Basic Neural Network knowledge

Description

Welcome to the comprehensive course on Large Language Models (LLMs)! This course is designed to take you through the journey of understanding what LLMs are, how they work, and how to implement and manage them effectively. By the end of this course, you will have a solid understanding of LLMs and be well-prepared to use them in practical scenarios.What You Will Learn:The position of LLMs within the broader field of Artificial Intelligence.The history and evolution of LLMs from early Natural Language Processing (NLP) techniques to advanced Deep Learning models.The significance of large datasets in pretraining LLMs and why these models are termed "large."Key components of LLMs, including tokenization, transformer architecture, and the attention mechanism.Different types of LLM architectures, such as GPT, BERT, and PaLM.Detailed insights into the training data, bias, and fairness issues, and popular datasets used in LLMs. How to handle model weights, understand their creation, and storage formats.Practical guidance on running LLMs offline, including hardware requirements and suitable models.An in-depth analysis of KoboldCPP and other relevant tools. Comprehensive understanding of tuning and sampling parameters and their impact on LLM performance. The final recap of key concepts, addressing misconceptions, and exploring the future opportunities and threats in the realm of LLMs.

Overview

Section 1: Introduction to LLMs

Lecture 1 Lecture 1 - Introduction to LLMs

Lecture 2 Topic Zoom 1 - Key LLMs in the Market

Lecture 3 Lecture 2 - Basic Terminology needed for this course

Lecture 4 Lecture 3 - How do I keep up?!

Section 2: LLMs in YOUR PC! - Running LLMs Locally! - Practical Lecture 1

Lecture 5 Getting the Software!

Lecture 6 More about Kobold and LLM Models

Lecture 7 Running LLMs LOCALLY! - A practical Lecture!

Section 3: Connecting Everything We've Learned So Far

Lecture 8 Where we are going! - A better understanding about what's next.

Section 4: Before Transformers!

Lecture 9 A brief History - RNNs, LSTMs, Vanishing Gradients! - Before Transformers!

Lecture 10 Topic Zoom 2 - A bit more about RNNs!

Section 5: Attention! - The transformer Architecture In Depth!

Lecture 11 Introduction

Lecture 12 BBYCROFT.NET - Simulation!

Lecture 13 Parts! - Journey of a Token Through a Transformer - Using BBYCROFT simulation!

Section 6: Life Cycle of the GGUF file - The First steps to making an LLM

Lecture 14 Introduction to this section

Lecture 15 Data Collection and Curation

Lecture 16 Data Preprocessing for LLMs

Lecture 17 Designing an LLM

Section 7: Architecture Design

Lecture 18 Architecture Design

Lecture 19 LLM Designing: 1. Design for Chad the Coder

Lecture 20 LLM Designing: 2. Design for Rowling the Writer

Lecture 21 LLM Designing: 3. Design for Newton the Researcher

Lecture 22 Conclusion of Architecture Design

Section 8: Training your LLM!

Lecture 23 Pretraining - A general Overview

Lecture 24 Pretraining - In Depth

Lecture 25 Finetuning

Section 9: Serving your LLM!

Lecture 26 Quantization

Lecture 27 Quantization Deep Dive

Lecture 28 Serving your LLM

Lecture 29 KoboldCPP and LLM Deployement

Section 10: Latest innovations

Lecture 30 Latest innovations - 1

Section 11: Final Quiz

Lecture 31 Conclusion!

Beginners who want to understand the basics of Large Language Models.,Students and professionals looking to get a foundational understanding of LLMs without requiring prior coding experience.,Anyone interested in AI and how advanced language models are developed and applied.