Apple ML Research Introduces IDEA Prune Pipeline for Efficient Generative Language Models
· By Antonio Sedino, CTRO · Published by Reinventy Solutions Corp.
Structured pruning pipelines show promise in token efficiency compared to training target-size models from scratch, advocating incorporation of enlarged 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. The paper advocates incorporating enlarged model pretraining as part of a structured pruning approach to improve token efficiency in generative language models.
Read the original source at Apple Machine Learning Research ↗