KATTA HAJMLI TRANSFORMER TIL MODELLARIDA HISOBLASH RESURSLARINI SAMARALI BOSHQARISH
Keywords:
neural language models, computational optimization, parallel processing, lightweight fine-tuning, model compression, deployment efficiencyAbstract
The computational demands of contemporary transformer models present significant challenges in deploying large-scale neural networks. This research synthesizes methodologies for improving efficiency in architecture design, training protocols, and deployment strategies.References
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.
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.
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Published
2026-01-04
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