KATTA HAJMLI TRANSFORMER TIL MODELLARIDA HISOBLASH RESURSLARINI SAMARALI BOSHQARISH

KATTA HAJMLI TRANSFORMER TIL MODELLARIDA HISOBLASH RESURSLARINI SAMARALI BOSHQARISH

Mualliflar

Kalit so‘zlar:

neyron til modellari, hisoblash optimallashtiruvi, parallel hisoblash, engil sozlash, model siqish, joylashtirish samaradorligi

Annotatsiya

Zamonaviy transformer modellarining hisoblash talablari katta hajmdagi neyron tarmoqlarni joriy etishda jiddiy qiyinchiliklar tug’diradi. Ushbu tadqiqot arxitektura dizayni, o‘qitish protokollari va joylashtirish strategiyalarida samaradorlikni oshirish metodologiyalarini sintez qiladi.

Adabiyotlar

1. Brown T., Mann B., Ryder N., et al. Language models are few-shot learners. Advances in Neural Information Processing Systems. 2020; 33:1877-1901.
2. Child R., Gray S., Radford A., Sutskever I. Generating long sequences with sparse transformers. arXiv preprint arXiv:1904.10509. 2019.
3. Ochilov M.A., Juraev F.D., Maxmatqulov G.X., Rahimov A.M. Analysis of important factors in checking the optimality of an indeterminate adjuster in a closed system. Journal of Critical Review. 2020;7(15):1679-1684.
4. Kaplan J., McCandlish S., Henighan T., et al. Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. 2020.
5. Beltagy I., Peters M.E., Cohan A. Longformer: The long-document transformer. arxiv preprint arxiv:2004.05150. 2020.
6. Jo’rayev, F. D. S., & Ochilov, M. A. (2023). Algorithms for multi-factory polynomial modeling of technological processes. Chemical Technology, Control and Management, 2023(1), 59-67.

Yuklab olishlar

Nashr etilgan

2026-01-04
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