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Transformers

Hugging Face's Python library of model definitions for text, vision, audio and multimodal machine learning, used for both inference and training.

Transformers

Transformers is a model-definition framework for machine learning models across text, computer vision, audio, video and multimodal tasks. A model defined in Transformers works with most training frameworks, such as DeepSpeed and PyTorch Lightning, inference engines such as vLLM and TGI, and libraries such as llama.cpp and mlx.

Its Pipeline API is a high-level class for inference that handles preprocessing and output for tasks such as text generation, speech recognition, image classification and visual question answering. Over a million model checkpoints on the Hugging Face Hub can be used with it.

It requires Python 3.10+ and PyTorch 2.5+, and is Apache-2.0 licensed.

More in Models

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    github.com

    A continual learning language model that trains from scratch on a single 8 GB VRAM GPU, assembling its own architecture and adapting its size to the hardware and data available rather than shipping as a fixed pretrained model.

  • An open-source contrastive-embedding pipeline that maps technical CVE vulnerability descriptions to formal cybersecurity compliance controls in NIST SP 800-53 and CMMC, published with its model generation, fine-tuning and evaluation code.

  • peekaboolean

    github.com

    A small vision-language model, fine-tuned with LoRA on a 500-million-parameter backbone, that answers typed multiple-choice, ordinal-score and yes/no questions about an image with calibrated probabilities, in about 400 milliseconds on a laptop.