Releases

Hugging Face released Tokenizers v1, a major milestone for its Rust-based tokenization stack.

Transformers v5 entered release candidate with simplified model definitions, standardized attention interfaces, and support for more than 400 architectures.

The core Hugging Face Hub Python client reached v1.0 with a redesigned hf CLI, httpx networking, and Xet-based file transfers.

Hugging Face launched a unified inference layer that lets developers call models across multiple providers through one API and billing experience.

Hugging Face released smolagents, a lightweight open-source framework for tool-using and code-executing AI agents.

Gradio 5 introduced redesigned application theming, stronger security defaults, and improved AI app development workflows.

Accelerate reached its 1.0 release-candidate milestone after becoming a core distributed-compute layer in the Hugging Face ecosystem.

Hugging Face launched LeRobot to bring open models, datasets, policies, and reproducible tooling to real-world robotics.

Hugging Face released a production-grade server for serving text embedding and reranking models.

Hugging Face released Candle, a minimalist Rust framework for fast and portable machine learning.

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

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

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.

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

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

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

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

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

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

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

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

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.