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Techniques for distributing computational tasks across multiple processors to accelerate machine learning workloads.
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Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and
Coordinates data, pipeline, and tensor parallelism to scale large-scale model training across multi-node clusters.