Run modern AI models for text, image, and audio with a single line of code
This tool standardizes the definitions of pretrained models that process various data types, including text, images, and audio. It allows you to load and run models using a common language, eliminating the need to handle hundreds of different model architectures with separate code. As a result, researchers and developers can quickly apply the latest AI features without diving deep into the internal structure of the models.
The central axis of the ecosystem
The tool manages model definitions centrally so that the entire ecosystem understands models based on the same standard. When a specific model definition is supported, it is designed to be compatible with training frameworks like PyTorch and JAX, inference engines like vLLM, and adjacent libraries like llama.cpp. This integration allows model developers to write a single piece of code and reuse it across various environments. Currently, over one million model checkpoints are shared on the Hugging Face Hub based on this standard.
First run with the Pipeline API
It provides Pipeline, a high-level inference class that allows you to perform tasks without directly configuring the model. It supports various tasks such as text generation, speech recognition, image classification, and visual question answering, automatically handling input preprocessing and output formatting. For example, if you specify a text generation task and enter a model name, the model is downloaded, cached, and then returns a result after receiving a prompt. For conversational models, you can also interact in the same way by constructing conversation history between the user and the system.
Scope and limitations
The tool is designed to deliver high performance in natural language understanding and generation, computer vision, audio, video, and multimodal tasks. By sharing pretrained models, it contributes to reducing the computational costs and carbon footprint associated with training from scratch. However, it does not serve as a toolbox for modularizing neural network components for free combination. The code in model files is intentionally written without additional abstraction to help researchers iterate experiments quickly on each model. Additionally, the training API is optimized for PyTorch models, so using other libraries like Accelerate is recommended for general machine learning loops.
Pre-use checks
An environment with Python 3.10 or higher and PyTorch 2.5 or higher is required. To install, use a virtual environment manager such as venv or uv and run the pip install "transformers[torch]" command. You can install from source to reflect the latest changes or to contribute, but stability is not guaranteed in this case. Example scripts are provided for reference, and you may need to modify the code to fit specific use cases. If you need to deeply customize the detailed structure of a model, keep in mind that model files can be used independently from the library.
