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[CL] Upcycling Large Language Models into Mixture of Experts E He, A Khattar, R Prenger, V Korthikanti... [NVIDIA] (2024) https://t.co/Hmj6lYFWoy https://t.co/adWSqww5zU
🤖 From this week's issue: A visual guide exploring the concept of Mixture of Experts (MoE) and its application in large language models. https://t.co/7n5SxLXG7x
NVIDIA presents Upcycling Large Language Models into Mixture of Experts Finds that upcycling outperforms continued dense model training based on large-scale experiments using Nemotron-4 15B trained on 1T tokens https://t.co/lKEtbMeQX8 https://t.co/L4LiEKrWDm
NVIDIA has introduced a new method for enhancing large language models by upcycling them into Mixture of Experts (MoE). This approach, detailed in their latest research, involves upcycling the Nemotron-4 15B model on 1 trillion tokens. The findings indicate that upcycling outperforms continuous dense model training, achieving a 1.5% better loss in both coarse and fine MoE models. The study also introduced a virtual groups initialization scheme. This research highlights the potential for increased efficiency and effectiveness in large-scale language model training.
