A hands-on workshop designed to teach the fundamentals of Large Language Models by building a GPT training pipeline from scratch. Users will create a ~10M parameter model capable of generating text, covering everything from tokenization to transformer architecture.
Highlights
Builds a functional GPT model from the ground up
Covers essential components: tokenization, transformer architecture, and training loops
Optimized to train a 10M parameter model on a laptop in under an hour
Supports local execution (Mac, Linux, Windows) and Google Colab
auto-generated
angelos-p · via GitHub
Context
Audience
Developers and learners interested in AI who have basic Python proficiency
PyTorchnanoGPTTransformersPythonLarge Language Models
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