Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters

from Arxiv Papers · host Igor Melnyk

Tokenformer introduces a scalable architecture that enhances Transformers' efficiency by using token-parameter attention, allowing for incremental scaling without retraining, thus reducing computational costs significantly. https://arxiv.org/abs//2410.23168 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

Episode metadata supplied by the publisher feed · Published Nov 2, 2024

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Tokenformer: Rethinking Transformer Scaling with Tokenized Model Parameters

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