@DanKornas: Stop wiring a different integration every time you test a new pretrained model. Transformers is a model-definition fram…
Summary
Transformers is a model-definition framework providing unified APIs and centralized model definitions for running pretrained models across modalities, from checkpoint to inference or training.
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Stop wiring a different integration every time you test a new pretrained model.
Transformers is a model-definition framework for engineers and researchers training or running pretrained models across text, vision, audio, video, and multimodal tasks.
It helps you move from a model checkpoint to inference or training by providing unified APIs and centralized model definitions used across training frameworks, inference engines, and adjacent modeling libraries.
Key features: • Pipeline API – handles input preprocessing and returns task-appropriate output. • Multimodal coverage – supports text, audio, vision, video, and multimodal tasks. • Shared model definitions – connect supported models with tools such as Axolotl, DeepSpeed, vLLM, and llama.cpp. • Reusable checkpoints – downloads and caches your selected Hub model for later use. • Customizable internals – exposes model internals consistently and lets model files work independently for experiments.
It’s open-source (Apache 2.0 license).
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