LLM Basics and Transformers with Pytorch

Master the core ideas behind transformers and LLMs. Using PyTorch and Hugging Face, you’ll grasp attention, run quick inference, and fine‑tune a mini‑BERT model for real‑world text‑classification tasks.

Modern NLP is driven by transformers, yet many developers still treat them as black boxes. In this course, LLM Basics and Transformers with PyTorch, you’ll learn to demystify transformer models and use them in practice. First, you’ll explore why self‑attention replaced recurrent networks and how tokenization, positional encoding, and encoder/decoder stacks work together. Next, you’ll discover how to load pre‑trained transformer checkpoints in PyTorch, convert raw text into tensors, and run lightning‑fast inference. Finally, you’ll learn to fine‑tune a compact BERT model on a small labeled dataset, handling over‑fitting and evaluating accuracy. When you’re finished, you’ll possess the core transformer knowledge—and hands‑on PyTorch examples—needed to explain, deploy, and adapt LLM technology in your own projects.