Hugging Face Tokenizers

A machine-learning library used with PyTorch for basic inference and as a reference implementation of model forward passes.

Hugging Face is an AI and machine-learning collaboration platform that hosts and distributes models, datasets, and applications. Its Hub supports public model, dataset, and application repositories across text, image, video, audio, and 3D workloads, while its open-source stack includes Transformers, Diffusers, Safetensors, Tokenizers, TRL, Transformers.js, smolagents, and PEFT. The platform provides a unified API for accessing models from AI providers, GPU-based compute and Inference Endpoints, and paid team and enterprise features such as private datasets, access controls, single sign-on, audit logs, and dedicated support.

Transformers is Hugging Face's open-source model-definition framework and Python library for inference and training with pretrained text, computer vision, audio, video, and multimodal machine-learning models. It centralizes model definitions behind a unified API and provides the Pipeline interface, which preprocesses inputs and returns task-specific outputs for applications such as text generation, automatic speech recognition, image classification, and visual question answering. Models can be downloaded and cached from the Hugging Face Hub, and the library supports moving models between PyTorch, JAX, and TensorFlow workflows while exposing model internals for customization. It requires Python 3.10 or later and PyTorch 2.5 or later for the documented installation.

The Hugging Face LLM Course is a free educational course covering large language models, natural language processing, and the Hugging Face ecosystem. It teaches Transformer concepts and use of Transformers, Datasets, Tokenizers, Accelerate, and the Hugging Face Hub; later chapters cover classic NLP tasks, model demos, fine-tuning, dataset curation, and reasoning models. The course includes code that can be run in Google Colab or Amazon SageMaker Studio Lab, requires good Python knowledge, and recommends prior introductory deep-learning study. It is released under the Apache 2 license, with contributions supported through issues, translations, and the course repository.

Gigatoken is an open-source tokenizer for language-model training data, implemented in Rust and distributed as a Python package. It tokenizes large text corpora at gigabytes per second, supports common tokenizer formats across x86 and ARM hardware, and provides compatibility modes for Hugging Face Tokenizers and Tiktoken. Its native API can read text files directly and encode them with parallel processing, while the compatibility modes are designed as drop-in replacements but incur additional overhead. The repository reports throughput of up to roughly 1,000 times that of Hugging Face tokenizers in its benchmarks.