Deepseek Introduces Manifold-Constrained Hyper-Connections for Efficient Large Language Model Training
Brief news summary
Chinese AI company Deepseek has introduced a new training method called Manifold-Constrained Hyper-Connections (mHC), aiming to enhance the efficiency and reduce the cost of training large language models. This approach builds on the Hyper-Connections technology originally developed by Bytedance in 2024, which itself is based on the ResNet architecture from Microsoft Research Asia. Deepseek’s mHC method offers more stable and scalable training processes without adding computational overhead, thanks to targeted infrastructure-level optimizations. The company has successfully tested mHC on language models with up to 27 billion parameters, demonstrating promising results that could impact the future development of AI by making large model training more accessible and efficient.Chinese AI company Deepseek has introduced a novel training technique called Manifold-Constrained Hyper-Connections (mHC), which aims to enable more efficient and cost-effective training of large language models, according to the South China Morning Post. This approach is an advancement of the original Hyper-Connections concept, which Bytedance developed in 2024. That technology itself is based on the classic ResNet architecture created by Microsoft Research Asia. Deepseek states that mHC achieves more stable and scalable training without raising computational expenses, thanks to targeted optimizations at the infrastructure level.
The researchers have successfully tested the method on models with up to 27 billion parameters, yielding positive outcomes.
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Deepseek Introduces Manifold-Constrained Hyper-Connections for Efficient Large Language Model Training
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