huggingface/transformers

Project overview

Transformers centralizes state-of-the-art model definitions for text, vision, audio, video, and multimodal tasks, providing a unified API for inference and training. It targets AI engineers, researchers, and developers who need to share trained checkpoints and move models across PyTorch, JAX, and TensorFlow.

Project type

Model Development · Model Runtime

Deployment

Library integration

License

Apache-2.0

Best for

• AI engineers, researchers, and developers who need to run inference or train state-of-the-art models using a unified API across multiple frameworks.

Key capabilities

• Centralizes state-of-the-art model definitions for text, vision, audio, video, and multimodal models for both inference and training.
• Handles preprocessing inputs and returning appropriate outputs for high-level inference across text, audio, vision, and multimodal tasks.
• Allows running a local serving instance and chatting with models directly from the command line.
• Move a single model between PyTorch, JAX, and TF2.0 frameworks at will.

Limitations and risks

• The library is not intended to be used as a modular toolbox of fundamental building blocks for neural networks.
• Installing Transformers directly from source may not be stable.

Getting started

• Installation difficulty is rated as easy via standard Python package installation using pip. Users should create a virtual environment (e.g., python -m venv .my-env) and install the package (e.g., pip install "transformers[torch]").

Evidence and sources

• README: Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer vision, audio, video, and multimodal models, for both inference and t…
• GitHub project description: 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
• README: Low barrier to entry for researchers, engineers, and developers.
• Release: Patch release v5.13.1
• README: The `Pipeline` is a high-level inference class that supports text, audio, vision, and multimodal tasks. It handles preprocessing the input and returns the appropriate output.

Reviewed at: 2026-07-16T10:44:52Z