PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
arXiv:2410.05265v1 Announce Kind: cross Summary: Quantization is important for deploying Massive Language Fashions (LLMs) by enhancing reminiscence effectivity and inference ...
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[2409.18878] Suicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
[Submitted on 27 Sep 2024 (v1), last revised 3 Oct 2024 (this version, v2)] View a PDF of the paper ...
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Improve Factuality via Implicit Self-consistency

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
[Submitted on 2 Oct 2024 (v1), last revised 3 Oct 2024 (this version, v2)] Authors:Yi Cheng, Xiao Liang, Yeyun Gong, ...
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[2404.01569] Evaluating Large Language Models Using Contrast Sets: An Experimental Approach

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
[Submitted on 2 Apr 2024 (v1), last revised 2 Oct 2024 (this version, v2)] View a PDF of the paper ...
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[2409.19663] Identifying Knowledge Editing Types in Large Language Models

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
[Submitted on 29 Sep 2024 (v1), last revised 1 Oct 2024 (this version, v2)] View a PDF of the paper ...
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Vision-Language Model Fine-Tuning via Simple Parameter-Efficient Modification

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
arXiv:2409.16718v1 Announce Sort: cross Summary: Current advances in fine-tuning Imaginative and prescient-Language Fashions (VLMs) have witnessed the success of immediate ...
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[2407.17844] Innovative Speech-Based Deep Learning Approaches for Parkinson’s Disease Classification: A Systematic Review

PrefixQuant: Static Quantization Beats Dynamic through Prefixed Outliers in LLMs
[Submitted on 25 Jul 2024 (v1), last revised 24 Sep 2024 (this version, v4)] View a PDF of the paper ...
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