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TorchScale: Transformers at Scale

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arxiv 2211.13184 v1 pith:3RCYJBPZ submitted 2022-11-23 cs.LG cs.CL

classification cs.LGcs.CL
keywords torchscaletransformersmodelingscaleopen-sourcetrainingachievedacross
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Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with better parallelization. In this work, we present TorchScale, an open-source toolkit that allows researchers and developers to scale up Transformers efficiently and effectively. TorchScale has the implementation of several modeling techniques, which can improve modeling generality and capability, as well as training stability and efficiency. Experimental results on language modeling and neural machine translation demonstrate that TorchScale can successfully scale Transformers to different sizes without tears. The library is available at https://aka.ms/torchscale.

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  1. RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection

    cs.SD 2025-07 conditional novelty 6.0 of 10

    A single transformer model aligns scores to performances, transcribes piano audio, and detects mistakes, including faithful handling of repeat sections without pre-unfolded scores.

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