Hugging Face

Hugging Face is a machine learning platform and open-source company whose Hub hosts the models, datasets, and demo apps that developers download, publish, and run:

Clément Delangue, Julien Chaumond, and Thomas Wolf founded the company in New York in 2016 to build a chatbot, then pivoted to the models underneath it. Open-sourcing that internal code produced Transformers, the library that made the company’s Hub a widely used place to share machine learning work.

The Hub now carries nearly three million models and about a million datasets, alongside more than 1.4 million Spaces, which are hosted demo apps that anyone can try from a browser. Repositories are Git-backed, so publishing a fine-tuned checkpoint works much like pushing code.

For Python developers, Transformers is the usual entry point. Its pipeline() helper wraps tokenization, model loading, and postprocessing behind a single call, so text generation or speech transcription takes a few lines. Version 5 dropped TensorFlow and JAX to target PyTorch alone and needs Python 3.10 or later.

Companion libraries cover the rest of the workflow. datasets loads corpora, peft and trl handle fine-tuning, accelerate spreads training across GPUs, and huggingface_hub ships the hf command for moving files in and out of repositories. Free accounts host public work, while paid tiers add team controls and Inference Endpoints run models on rented GPUs.

Official website: huggingface.co

Related Resources

Tutorial

Hugging Face Transformers: Leverage Open-Source AI in Python

Get hands-on with Hugging Face Transformers in Python. Download, run, and inspect pretrained open-source AI models, and speed up inference on GPUs.

intermediate ai

For additional information on related topics, take a look at the following resources:

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