Releases

Hugging Face introduced SafeTensors as a fast tensor serialization format designed to avoid arbitrary-code execution.

Hugging Face acquired Gradio, bringing its open-source interface framework and team into the company while keeping the project open source.

Hugging Face launched Spaces with native Gradio support, allowing community members to host interactive machine-learning demos on the Hub.

Hugging Face launched Optimum to optimize Transformers models for specialized hardware and inference runtimes.

Hugging Face launched AutoTrain to automate model training and fine-tuning from user-provided datasets.

Hugging Face introduced a paid individual plan with expanded private, storage, and compute capabilities on the Hub.

Hugging Face helped launch the BigScience open research workshop, coordinating an international collaboration to train a large multilingual language model.

Hugging Face launched Accelerate to let PyTorch code run across distributed hardware with minimal changes.

Hugging Face raised $40 million in Series B funding led by Addition to grow its open-source machine-learning ecosystem and hosted platform.

Transformers v4 began its release cycle with broad framework and architecture support and a standardized model API.

Hugging Face began its hosted inference platform with a serverless API for models on the Hub, later expanding into dedicated endpoints and provider routing.

Hugging Face introduced a hosted API for running models from the Hub without managing serving infrastructure.

Hugging Face launched hosted dataset repositories alongside its Datasets library, creating a shared catalog for reusable machine-learning data.

Hugging Face released a unified open-source library and catalog for loading, processing, streaming, and sharing datasets.

Hugging Face made model repositories a central Hub product for versioning, documenting, discovering, and reusing pretrained models.

Hugging Face expanded from an NLP library into a Git-based collaboration platform for sharing models and machine-learning artifacts.

Hugging Face introduced its high-performance Rust-backed Tokenizers library for production-scale text preprocessing.

Hugging Face raised $15 million in a Series A led by Lux Capital to expand its open-source natural-language-processing platform.

Hugging Face released DistilBERT, a smaller and faster distilled BERT model retaining most of the teacher model’s language-understanding performance.

Hugging Face renamed pytorch-transformers to Transformers and expanded the library’s framework and architecture support.

Gradio launched as an open-source Python framework for rapidly building browser interfaces around machine-learning models.

Hugging Face released an open-source PyTorch implementation and pretrained checkpoints for BERT, the project that evolved into the Transformers library.

Hugging Face raised a $4 million seed round backed by investors including A.Capital, Betaworks, SV Angel, and Thirty Five Ventures.

Hugging Face raised approximately $1.2 million in early angel funding to develop its conversational AI startup.

Clément Delangue, Julien Chaumond, and Thomas Wolf founded Hugging Face in New York, initially building a conversational AI companion before pivoting toward open machine learning.