This repository provides a clean implementation of the MAML meta-learning algorithm in PyTorch. MAML learns model parameters such that few gradient steps on a new task produce good generalization. The implementation supports supervised learning experiments on MiniImagenet (few-shot classification) and Omniglot datasets, including both the full MAML algorithm and First-Order approximation (Reptile) variants.