Releases

Hugging Face raised $235 million in a Series D led by Salesforce Ventures at a $4.5 billion post-money valuation, with participation from major cloud and semiconductor companies.

Hugging Face released IDEFICS, an 80B open-access visual-language model supporting sequences of images and text.

Hugging Face launched managed training and deployment offerings in AWS Marketplace, simplifying enterprise procurement and use on AWS.

NVIDIA and Hugging Face announced a partnership to connect Hub developers with NVIDIA DGX Cloud for training and tuning generative AI models.

Hugging Face introduced Collections for curating and sharing related models, datasets, Spaces, and papers.

Hugging Face launched a daily research-paper discovery feed connected to community profiles, models, datasets, and discussions.

Hugging Face launched an open and reproducible benchmark leaderboard for comparing community language models.

BigCode released StarCoder, a 15.5B-parameter code model trained on permissively licensed code from The Stack.

Hugging Face launched HuggingChat, an open-source conversational interface initially powered by an OpenAssistant model.

AWS and Hugging Face announced a strategic collaboration to make generative AI more accessible and optimize Transformers training and deployment on AWS infrastructure.

Hugging Face expanded its organization offering with team and enterprise plans for private repositories, access control, governance, and support.

Hugging Face released Parameter-Efficient Fine-Tuning tools for adapting large models with methods such as LoRA.

BigScience released BLOOMZ and mT0, instruction-finetuned multilingual models derived from BLOOM and the xP3 task collection.

The BigCode project released The Stack, a large permissively licensed source-code dataset with tools for governance and opt-out.

Hugging Face launched managed, autoscaling dedicated endpoints for deploying models from the Hub.

Hugging Face released Text Generation Inference, a production server optimized for large-language-model generation.

Diffusers added first-class support for Stable Diffusion, enabling local and hosted generation workflows around the newly released open model.

Hugging Face launched its enterprise platform around Private Hub, secure collaboration, governance, deployment, and organizational support.

Hugging Face launched Private Hub as a secure and compliant platform for organizations to collaborate on models, datasets, and applications.

Hugging Face released Transformer Reinforcement Learning tooling for post-training language models with supervised, preference, and reinforcement-learning methods.

Hugging Face launched Diffusers, an open-source library providing modular pipelines for diffusion-based generative models.

The BigScience workshop released BLOOM, a 176-billion-parameter multilingual language model trained on the Jean Zay supercomputer.

Hugging Face launched Evaluate as a unified framework for model and dataset evaluation.

Hugging Face raised $100 million in Series C funding led by Lux Capital at a reported $2 billion post-money valuation.

Hugging Face introduced browser previews and automatic processing for datasets hosted on the Hub.

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.