IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token efficiency compared to training target-size models from scratch. In this paper, we advocate incorporating enlarged model pretraining, which is often ignored in previous works, into pruning. We study the...
What happened
Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token efficiency compared to training target-size models from scratch. In this paper, we advocate incorporating enlarged model pretraining, which is often ignored in previous works, into pruning. We study the...
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- Apple Machine Learning ResearchPrimary source · Supports · EN · 100%IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining ↗
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- IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining Observed
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