Hugging Face Transformers

Tracked

The model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal, for both inference and training.

Author Hugging Face Open Sourced 2018-10-29 Last Commit Unknown

Overview

Hugging Face Transformers is the foundational framework for modern AI/ML development, providing access to thousands of pretrained models for text, vision, audio, and multimodal tasks. It serves as the de facto standard library for working with transformer-based models, supporting both inference and training across PyTorch, TensorFlow, and JAX.

Key Features

Unified API for 200,000+ pretrained models across text, vision, audio, and multimodal
Support for PyTorch, TensorFlow, and JAX backends
Built-in pipelines for common NLP, computer vision, and audio tasks
Seamless integration with Hugging Face Hub for model sharing and collaboration
Native support for quantization, compilation, and optimization techniques

Use Cases

Building AI applications with pretrained language, vision, and audio models
Fine-tuning foundation models for domain-specific tasks
Creating multimodal AI pipelines combining text, image, and audio
Prototyping and productionizing transformer-based systems

Technical Details

Pure Python library with extensive model architecture implementations
Supports model quantization (bitsandbytes, GPTQ, AWQ) and compilation (torch.compile)
Integrates with Hugging Face ecosystem: Datasets, Tokenizers, Accelerate, PEFT, TRL