Hugging Face Transformers is a Python library that abstracts the complexity of loading, fine-tuning, and deploying transformer-based NLP models. It provides unified APIs for tokenization, model inference, and training across BERT, GPT, T5, Llama, and hundreds of other checkpoints. Models are versioned on Hugging Face Hub, a registry of 500k+ community-contributed checkpoints. Instead of building model architecture from scratch, you load a pre-trained checkpoint in 3 lines of code, fine-tune on your data, and deploy. The library handles device management (CPU/GPU/TPU), distributed training, and model serialization.