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[short] MEDUSA: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
This paper introduces MEDUSA, a method for improving inference in Large Language Models (LLMs) by adding extra decoding heads to predict multiple tokens in parallel. MEDUSA achieves significant speedup without compromising generation quality.
https://arxiv.org/abs//2401.10774
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
This paper introduces MEDUSA, a method for improving inference in Large Language Models (LLMs) by adding extra decoding heads to predict multiple tokens in parallel. MEDUSA achieves significant speedup without compromising generation quality.
https://arxiv.org/abs//2401.10774
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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