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DeepNet – Scaling Transformers to 1,000 Layers: The Next Frontier in Deep Learning Introduction In recent years, Transformers have become the backbone of state-of-the-art models in both NLP and computer vision, powering systems like BERT, GPT, and LLaMA. However, as these models grow deeper, stability becomes a significant hurdle. Traditional Transformers struggle to remain stable beyond a few dozen layers. DeepNet, a new Transformer architecture, addresses this challenge by using a technique called DeepNorm, which stabilizes training up to 1,000 layers. To address this, DeepNet introduced the DeepNorm technique, which modifies residual connections to stabilize training for Transformers up to 1,000 layers. researchgate.net Building upon these advancements, recent research has proposed new methods to further enhance training stability in deep Transformers. For instance, the Stable-Transformer model offers a theoretical analysis of initialization methods, presenting a more stable approach that prevents gradient explosion or vanishing at the start of training. openreview.net Additionally, the development of TorchScale, a PyTorch library by Microsoft, aims to efficiently scale up Transformers. TorchScale focuses on various aspects, including stability, generality, capability, and efficiency, to facilitate the training of deep Transformer models. github.com These innovations reflect the ongoing efforts in the AI research community to overcome the...