GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients

Aashiq Muhamed; Oscar Li; David Woodruff; Mona Diab; Virginia Smith

doi:10.18653/v1/2024.emnlp-main.835

GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients

Aashiq Muhamed, Oscar Li, David Woodruff, Mona T. Diab, Virginia Smith

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Anthology ID:: 2024.emnlp-main.835
Volume:: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:: November
Year:: 2024
Address:: Miami, Florida, USA
Editors:: Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:: EMNLP
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Publisher:: Association for Computational Linguistics
Note:
Pages:: 14978–15003
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URL:: https://aclanthology.org/2024.emnlp-main.835/
DOI:: 10.18653/v1/2024.emnlp-main.835
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Cite (ACL):: Aashiq Muhamed, Oscar Li, David Woodruff, Mona T. Diab, and Virginia Smith. 2024. GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14978–15003, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):: GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients (Muhamed et al., EMNLP 2024)
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PDF:: https://aclanthology.org/2024.emnlp-main.835.pdf

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@inproceedings{muhamed-etal-2024-grass,
    title = "{GRASS}: Compute Efficient Low-Memory {LLM} Training with Structured Sparse Gradients",
    author = "Muhamed, Aashiq  and
      Li, Oscar  and
      Woodruff, David  and
      Diab, Mona T.  and
      Smith, Virginia",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.835/",
    doi = "10.18653/v1/2024.emnlp-main.835",
    pages = "14978--15003"
}

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%0 Conference Proceedings
%T GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients
%A Muhamed, Aashiq
%A Li, Oscar
%A Woodruff, David
%A Diab, Mona T.
%A Smith, Virginia
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F muhamed-etal-2024-grass
%R 10.18653/v1/2024.emnlp-main.835
%U https://aclanthology.org/2024.emnlp-main.835/
%U https://doi.org/10.18653/v1/2024.emnlp-main.835
%P 14978-15003

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Markdown (Informal)

[GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients](https://aclanthology.org/2024.emnlp-main.835/) (Muhamed et al., EMNLP 2024)

GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients (Muhamed et al., EMNLP 2024)

ACL

Aashiq Muhamed, Oscar Li, David Woodruff, Mona T. Diab, and Virginia Smith. 2024. GRASS: Compute Efficient Low-Memory LLM Training with Structured Sparse Gradients. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 14978–15003, Miami, Florida, USA. Association for Computational Linguistics.