Hugging Face is a collaborative platform for machine learning models, datasets, and applications.

The Model Hub contains over 500,000 pretrained models. Each entry shows architecture details, benchmark scores, and sample code. Users can download weights directly or load them via the Transformers library in a single line.

Datasets Hub offers versioned data with browser previews. Load any dataset with the Datasets library; it streams large files and caches locally. Filters include size, task, and license.

Spaces host interactive demos using Gradio or Streamlit. The free tier runs on CPU; paid upgrades add GPU acceleration, starting at $0.60 per hour. Deployment takes one click from the repo.

Enterprise plans provide private repositories, SSO, and dedicated inference endpoints. The Inference API accesses models without requiring infrastructure management.

Key libraries include Transformers for NLP and vision, Diffusers for generation, and Accelerate for training. All support PyTorch, TensorFlow, and JAX. Community contributions drive updates.

Compared to GitHub, Hugging Face adds model cards and built-in inference. Kaggle focuses on competitions rather than sharing. The search function works, but it returns many similar models; use task filters to narrow down the results.