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Hugging Face Transformers

huggingface/transformers

A model-definition framework for state-of-the-art machine learning models across text, vision, audio, and multimodal domains, supporting both inference and training.

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Python언어
Apache-2.0라이선스
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프로젝트 스크린샷

Hugging Face Transformers 스크린샷 Hugging Face Transformers 스크린샷

개요

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.

주요 기능

  • 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

요구 사항, 설치 및 빠른 시작

Create and activate a virtual environment using venv or uv. Install Transformers with PyTorch support via pip using 'pip install "transformers[torch]"' or via uv using 'uv pip install "transformers[torch]"'. For the latest changes, install from source by cloning the repository and running 'pip install ".[torch]"'.

사용 정보

Instantiate a pipeline by specifying a task and model, then pass input data to get results. For example, use 'pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")' for text generation. For chat-based interactions, construct a chat history and pass it to the pipeline.

모델 호환성 및 사용 사례

Supports a wide range of models including Qwen, Llama, Whisper, DINOv2, BLIP, Mixtral, BART, T5, Gemma, and many others across various modalities.

라이선스 및 위험 참고 사항

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.

릴리스 및 유지 관리

Not stated in the repository metadata.

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