Releases

Transformers added support for running llama.cpp GGUF quantized models within the library’s model-definition and inference workflows.

HF Jobs added exposed-port workflows for running private services such as OpenAI-compatible vLLM servers directly on Hugging Face compute.

Hugging Face launched mutable, Xet-backed S3-like Storage Buckets for checkpoints, logs, traces, and intermediate AI data.

Hugging Face discontinued HUGS model-deployment containers and directed users toward enterprise collections and other deployment paths.

The launch of HF Jobs established an on-demand compute layer alongside the Hub’s storage, collaboration, and inference products.

Hugging Face launched pay-per-use compute jobs that run scripts and containers on CPUs and GPUs directly through the hf CLI.

Hugging Face and Pollen Robotics launched Reachy Mini, a compact open-source desktop robot aimed at developers, researchers, and educators.

Hugging Face began broad deployment of Xet-based storage and transfers across Hub repositories, bringing deduplication and faster large-file operations.

LeRobot introduced the SO-101, a refined low-cost open robot arm for collecting demonstrations and training robotic policies.

Hugging Face launched Open R1 to openly reproduce and extend the data, training, and evaluation pipeline behind reasoning models.

Text Generation Inference added a multi-backend architecture supporting vLLM and TensorRT-LLM alongside its native backend.

Hugging Face detailed a new storage and transfer architecture using Xet’s content-defined chunking to accelerate large repository operations.

Hugging Face launched HUGS, optimized zero-configuration deployment containers for open generative models.

The LeRobot community introduced the SO-100, a low-cost open robot arm design for data collection and imitation learning.

Hugging Face refreshed the Open LLM Leaderboard with a harder, contamination-resistant suite and new scoring methodology.

Hugging Face released FineWeb-Edu, an educationally filtered subset that showed strong gains for small-model pretraining.

Hugging Face released FineWeb, a 15-trillion-token filtered and deduplicated English web dataset for language-model pretraining.

Pollen Robotics launched Reachy 2, an open-source humanoid robot platform later brought into Hugging Face through its acquisition of Pollen.

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.

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

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

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.

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

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

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

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

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.