Projekt-Screenshots
Übersicht
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.
Wichtige Funktionen
- 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
Voraussetzungen, Installation und Schnellstart
Nutzung
Modellkompatibilität und Anwendungsfälle
Supports a wide range of models including Qwen, Llama, Whisper, DINOv2, BLIP, Mixtral, BART, T5, Gemma, and many others across various modalities.
Lizenz- und Risikohinweise
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.
Veröffentlichung und Wartung
Not stated in the repository metadata.