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TorchScale is a PyTorch library that allows researchers and developers to scale up Transformers efficiently and effectively.
It has the implementation of fundamental research to improve modeling generality and capability as well as training stability and efficiency of scaling Transformers.
Fundamental research to develop new architectures for foundation models and A(G)I, focusing on modeling generality and capability, as well as training stability and efficiency.
- Stability - [**DeepNet**](https://arxiv.org/abs/2203.00555): scaling Transformers to 1,000 Layers and beyond
- Generality - [**Foundation Transformers (Magneto)**](https://arxiv.org/abs/2210.06423): towards true general-purpose modeling across tasks and modalities (including language, vision, speech, and multimodal)
- Capability - A [**Length-Extrapolatable**](https://arxiv.org/abs/2212.10554) Transformer