huggingface/transformers

Project overview

The library serves as a pivot point across training frameworks and inference engines, lowering compute costs by sharing trained checkpoints and providing a low barrier to entry for state-of-the-art models.

Project type

Audio & Speech

Use cases

Meeting Notes · Audio & Speech

Deployment

Library integration

License

Apache-2.0

Best for

• AI engineers and researchers who need to run inference or training on centralized state-of-the-art models across text, vision, audio, and video using a unified API.

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.
• Enables moving a single model between PyTorch, JAX, and TF2.0 frameworks at will.

Limitations and risks

• The library is specifically not a modular toolbox of building blocks for neural networks, limiting its use for generic machine learning loop construction.
• Installing the Transformers library from source may not be stable and users should prefer standard Python package installation methods.

Getting started

• Create a virtual environment and install the package via pip. Note that installing from source may not be stable.

Evidence and sources

Open original GitHub project