Sparse LLMs in data-scarce multi-epoch regimes follow a scaling law based on active parameters, unique tokens, repetition count, and sparsity level that predicts performance and delays data saturation.
Enabling high-sparsity foundational Llama models with efficient pretraining and deployment.arXiv preprint arXiv:2405.03594,
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When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
Sparse LLMs in data-scarce multi-epoch regimes follow a scaling law based on active parameters, unique tokens, repetition count, and sparsity level that predicts performance and delays data saturation.