✨OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models
📝 Summary:
OmniZip is a training-free framework that addresses the computational bottleneck in omnimodal LLMs by dynamically compressing audio-visual tokens. It uses audio retention scores to guide video token pruning, achieving 3.42X inference speedup and 1.4X memory reduction without performance loss.
🔹 Publication Date: Published on Nov 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14582
• PDF: https://arxiv.org/pdf/2511.14582
• Github: https://github.com/KD-TAO/OmniZip
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#OmnimodalLLM #TokenCompression #LLMs #AI #ModelEfficiency
📝 Summary:
OmniZip is a training-free framework that addresses the computational bottleneck in omnimodal LLMs by dynamically compressing audio-visual tokens. It uses audio retention scores to guide video token pruning, achieving 3.42X inference speedup and 1.4X memory reduction without performance loss.
🔹 Publication Date: Published on Nov 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.14582
• PDF: https://arxiv.org/pdf/2511.14582
• Github: https://github.com/KD-TAO/OmniZip
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#OmnimodalLLM #TokenCompression #LLMs #AI #ModelEfficiency