Project screenshots
Overview
Transformers serves as a centralized model-definition framework that ensures compatibility across the broader machine learning ecosystem. By standardizing model definitions, it allows seamless integration with various training frameworks like Axolotl, Unsloth, DeepSpeed, and PyTorch-Lightning, as well as inference engines such as vLLM, SGLang, and TGI. The library provides a unified API with minimal abstractions, making it accessible for researchers and developers to leverage over 1 million pretrained model checkpoints available on the Hugging Face Hub.
Key features
- Unified API for pretrained models across text, vision, audio, and multimodal tasks
- Compatibility with major training frameworks and inference engines
- Access to over 1 million model checkpoints on the Hugging Face Hub
- High-level Pipeline API for easy inference across modalities
- Support for model customization and exposure of model internals
Requirements, installation and quick start
Usage
Model compatibility and use cases
Supports a wide range of models including Qwen, Llama, Whisper, DINOv2, BLIP, Mixtral, BART, T5, Gemma, and many others across various modalities.
License and risk notes
Licensed under the Apache License, Version 2.0.
Editorial verification 2026-08-09: repository URL, owner, description, license and repository statistics were reviewed. License metadata: Apache-2.0. README was fetched for the channel draft; re-check repository dependencies, releases and model terms before production use.
Release and maintenance
Not stated in the repository metadata.