Apple ML Research Introduce IDEA Prune Pipeline per Modelli Generativi di Lingua Efficienti
· By Antonio Sedino, CTRO · Published by Reinventy Solutions Corp.
Pipeline di potatura strutturata mostrano promessa nell'efficienza dei token rispetto all'addestramento di modelli di dimensioni target da zero, sostenendo l'incorporamento di un pre-addestramento del modello esteso.
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 ↗