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Retentive Network: A Successor to Transformer for Large Language Models
RetNet is proposed as a foundation architecture for large language models, achieving training parallelism, low-cost inference, and good performance. It supports three computation paradigms and shows favorable scaling results, making it a strong successor to Transformer.
https://arxiv.org/abs//2307.08621
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
RetNet is proposed as a foundation architecture for large language models, achieving training parallelism, low-cost inference, and good performance. It supports three computation paradigms and shows favorable scaling results, making it a strong successor to Transformer.
https://arxiv.org/abs//2307.08621
YouTube: https://www.youtube.com/@ArxivPapers
PODCASTS:
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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