What problem it solves

Machine learning workflows need a shared model definition so that models can work consistently across training frameworks, inference engines, and adjacent modeling libraries. Transformers centralizes these definitions and exposes a unified interface for loading pretrained models, running inference, and adapting model internals.

Key features

• Broad Model Support — The framework covers text, computer vision, audio, video, and multimodal models for inference and training.

  Source: README, src

• Unified Pipeline API — The high-level `Pipeline` inference class supports multiple modalities and tasks, handles input preprocessing, and returns task-appropriate output.

  Source: README

• Customizable Model Internals — Model internals are exposed consistently so that implementations can be reproduced, adapted, or used independently during experiments.

  Source: README

• Extensive Project Resources — The repository includes examples, tests, documentation, and a curated page of community projects built around Transformers and the Hugging Face Hub.

  Source: examples, tests, awesome-transformers.md

Quick start

Copied verbatim from the project's README.

# pip
pip install "transformers[torch]"

# uv
uv pip install "transformers[torch]"

from transformers import pipeline

pipeline = pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")
pipeline("the secret to baking a really good cake is ")
[{'generated_text': 'the secret to baking a really good cake is 1) to use the right ingredients and 2) to follow the recipe exactly. the recipe for the cake is as follows: 1 cup of sugar, 1 cup of flour, 1 cup of milk, 1 cup of butter, 1 cup of eggs, 1 cup of chocolate chips. if you want to make 2 cakes, how much sugar do you need? To make 2 cakes, you will need 2 cups of sugar.'}]

Who it may suit

This project may suit researchers, engineers, developers, students, and professors who work with pretrained models or need consistent model definitions across training and inference workflows.

Topics to discuss

• Which model definition or interoperability improvements would be most valuable to the community?

• How can contributors improve the consistency of examples and APIs across modalities?

• What changes would make custom model experimentation easier while preserving approachable abstractions?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/huggingface/transformers#readme

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