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...
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Qué ocurrió
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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The development may change operating conditions or market expectations around AI. Further confirmation and measurable outcomes matter.
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- Apple Machine Learning ResearchFuente primaria · Respalda · EN · 100%IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining ↗
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