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    Deep learning· 2022

    GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

    Elias Frantar (first author) · 2022

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    arXiv 2210.17323OpenAlex W4307934016

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    Why it matters

    Established accurate, efficient post-training 3–4-bit quantization of massive language models.

    Condensed from the expert lists' notes.

    The record

    First released
    2022 (from arXiv 2210.17323)
    Citations
    2,747 citations, as counted by Papers With Code (CC BY-SA 4.0)
    Named by
    both expert lists
    Score
    0.554 · computed rank 186 · consensus 0.77, must-read 0.5, citations percentile 0.781 (2020s · deep-learning), awards 0, existing lists 0. Method

    Data sources

    Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0). Each count names its source. See the method.