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[QA] Reducing Transformer Key-Value Cache Size with Cross-Layer Attention
Key-value caching in large language models is crucial for decoding speed. Multi-Query Attention (MQA) and Cross-Layer Attention (CLA) reduce memory usage while maintaining accuracy, enabling larger models.
https://arxiv.org/abs//2405.12981
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
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By Igor Melnyk
5
33 ratings
Key-value caching in large language models is crucial for decoding speed. Multi-Query Attention (MQA) and Cross-Layer Attention (CLA) reduce memory usage while maintaining accuracy, enabling larger models.
https://arxiv.org/abs//2405.12981
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
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