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# Model Summary
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## Background motivation
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The development of the latest neural network/deep learning model architectures (State-of-the-Art/SOTA) over the past two decades has seen a trend toward increasingly large-scale model development (in terms of the number of parameters, reaching billions, especially in LLM models), heavy computational burdens/requirement of capable hardware and infrastructure, large energy footprints, and large model sizes, as well as high and unsustainable memory and execution footprints. This may not be immediately apparent to end users, but it actually poses a real environmental threat.
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# Model Summary
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(directly go the [demo here](https://huggingface.co/spaces/febrifahmi/NoD-1-1D))
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## Background motivation
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The development of the latest neural network/deep learning model architectures (State-of-the-Art/SOTA) over the past two decades has seen a trend toward increasingly large-scale model development (in terms of the number of parameters, reaching billions, especially in LLM models), heavy computational burdens/requirement of capable hardware and infrastructure, large energy footprints, and large model sizes, as well as high and unsustainable memory and execution footprints. This may not be immediately apparent to end users, but it actually poses a real environmental threat.
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