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Transformers acts as the model-definition framework for state-of-the-art machine
learning models in text, computer vision, audio, video, and multimodal model,
for both inference and training.
It centralizes the model definition so that this definition is agreed upon
across the ecosystem. transformers is the pivot across frameworks: if a model
definition is supported, it will be compatible with the majority of training
frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...),
inference engines (vLLM, SGLang, TGI, ...), and adjacent modeling libraries
(llama.cpp, mlx, ...) which leverage the model definition from transformers.
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