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<a href="https://pypi.org/project/torchscale"><img alt="MIT License" src="https://badge.fury.io/py/torchscale.svg" /></a>
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<a href="https://pypi.org/project/torchscale"><img alt="MIT License" src="https://badge.fury.io/py/torchscale.svg" /></a>
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TorchScale is a PyTorch library that allows researchers and developeres to scale up Transformers efficiently and effectively.
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TorchScale is a PyTorch library that allows researchers and developers to scale up Transformers efficiently and effectively.
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It has the implemetention of fundamental research to improve modeling generality and capability, as well as training stability and efficiency of scaling Transformers.
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It has the implementation of fundamental research to improve modeling generality and capability, as well as training stability and efficiency of scaling Transformers.
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- Stability - [**DeepNet**](https://arxiv.org/abs/2203.00555): scaling Transformers to 1,000 Layers and beyond
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- Stability - [**DeepNet**](https://arxiv.org/abs/2203.00555): scaling Transformers to 1,000 Layers and beyond
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- Generality - [**Foundation Transformers (Magneto)**](https://arxiv.org/abs/2210.06423)
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- Generality - [**Foundation Transformers (Magneto)**](https://arxiv.org/abs/2210.06423)
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trademarks or logos is subject to and must follow
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trademarks or logos is subject to and must follow
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[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
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[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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Any use of third-party trademarks or logos are subject to those third-party's policies.
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Any use of third-party trademarks or logos are subject to those third-party's policies.
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