[QA] When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training

from Arxiv Papers · host Igor Melnyk

AnchorAttention addresses numerical issues in BFloat16 with Rotary Positional Embedding, enhancing long-context performance and training efficiency in large language models while maintaining task capabilities. https://arxiv.org/abs//2411.13476 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 23, 2024

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[QA] When Precision Meets Position: BFloat16 Breaks Down RoPE in Long-Context Training

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