Large Language Models: Training the Model with PyTorch
This course delves into the mathematical foundations and practical methodologies that drive large language model training, with a spotlight on LLaMA 3.2. Through precise derivations, visual aids, and hands‑on coding snippets, you’ll master training dynamics. The course also unpacks parameter‑efficient adaptation methods like LoRA and model quantization, exploring how they enable powerful model updates with minimal compute and memory overhead.
To reinforce these concepts, the course includes a hands‑on project that guides the learner through building an end‑to‑end training pipeline: preparing and tokenizing a specialized dataset, implementing LoRA and quantization in a fine‑tuning loop, and evaluating model outputs with standard metrics. By course end, learners will have executed a complete LLaMA 3.2 fine‑tuning workflow and gained practical expertise in training state‑of‑the‑art LLMs.
What you will learn:
- Architectural details of LLMs at an implementation level
- How to set up a Python development environment with PyTorch and necessary libraries
- Walk through and implement all subcomponents to train an LLM (Base LLM Architecture, Optimizer, Trainer, LoRA, Quantization, etc.)
- How to assemble the subcomponents to build the complete LLM model and trainer
- Testing the model with sample inputs to verify its functionality
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: 978-1-7281-7896-7