Large Language Models: Architecture Analysis and Implementation
This course covers the foundational principles and practical techniques needed to train state‑of‑the‑art large language models, with a particular focus on LLaMA 3.2. The learner will explore precise mathematical formulations and clear visualizations of core architectural innovations—Group Query Attention mechanisms, rotary positional embeddings, and byte‑pair encoding for efficient tokenization. The course then delves into design choices that enable processing very long contexts and scaling models effectively, all while managing computational and memory constraints. Learners will gain a deep understanding of how these components interact to deliver high‑performance text generation. To reinforce learning, the course includes a hands‑on programming exercise that guides you through implementing the full LLaMA 3.2 architecture incorporating Group Query Attention, rotary positional embeddings, byte‑pair encoding, and generating sample text with pre‑trained open‑source weights.
What you will learn:
- Foundational knowledge to get started with training LLMs
- Precise mathematical explanations and practice coding exercises
- Comprehensive understanding of both theoretical and practical insights focusing on architectural innovations in LLMs
- Unique components and design choices that distinguish large language models from other transformer-based architectures
- Group Query Attention, rotary positional embeddings, byte pair encoding and other key processes-enabled models to process very long text efficiently
- Practical implementation of the complete Llama 3.2 model
- Ability to generate some text using pre-trained open model weights
This course is part of the following course program:
Large Language Models Demystified
Instructors
Sai Chand Boyapati
Mr. Boyapati is an internationally recognized expert in software quality assurance (QA), whose groundbreaking work has had a transformative impact on industries worldwide. His influence and contribution in testing span major developments in software products that have redefined their markets.
Mr. Boyapati holds a critical leadership role as Director of Software Quality Assurance in a globally distinguished organization.
In addition to his technical achievements, Mr. Boyapati has served as a peer reviewer and judge in authoritative capacities. He has evaluated numerous research papers for prestigious conferences and hackathons on AI & LLM’s.
He has written extensively on QA, cybersecurity, and artificial intelligence/LLM’s, with articles published in Media. His book, Focus on QA: Redefining Software Testing in the AI-Driven Era, became a bestseller upon release, providing invaluable insights into applying AI to QA processes
Hamza Mohammed, Course Editor
Hamza Mohammed is a Machine Learning Engineer with Samsung Research America. He is an industry expert in deep learning and reinforcement learning, specializing in large language models. Mr. Hamza has a proven research and industry track record applying, optimizing, and accelerating, deep learning and reinforcement learning in various disciplines, including computer vision, natural language processing (including multi-modal modeling), robotics and automation, software engineering and testing, autonomous navigation and ADAS, digital twin simulation, and biomedical imaging. He has designed and optimized ML models and algorithms for edge-compute deployments and is an authority in securing and optimizing AI application security for on-device and on-premise environments. He is a contributor to several open-source projects, an author and peer-reviewer of multiple publications in top-tier ML venues, and is an inventor on key patents. Mr. Hamza holds a B.S. in Electrical Engineering and Computer Sciences, University of California, Berkeley, USA.
Who Should Attend:
AI Researchers and Academics, Data Scientists, Educators and Instructors, Enthusiasts with a Strong Programming Background , Machine Learning Engineers, Software Developers and Programmers, Students (experience in linear algebra and calculus required), Technical Professionals in industry, Technical Product Manager, Technical Leaders
Prerequisites: Basic programming knowledge in Python; no prior experience with language models is required
Course Level: Intermediate
Publication Year: 2026
ISBN: 978-1-7281-7895-0