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pro vyhledávání: '"Ehrlich, Ryan"'
Autor:
Brown, Bradley, Juravsky, Jordan, Ehrlich, Ryan, Clark, Ronald, Le, Quoc V., Ré, Christopher, Mirhoseini, Azalia
Scaling the amount of compute used to train language models has dramatically improved their capabilities. However, when it comes to inference, we often limit the amount of compute to only one attempt per problem. Here, we explore inference compute as
Externí odkaz:
http://arxiv.org/abs/2407.21787
Autor:
Juravsky, Jordan, Brown, Bradley, Ehrlich, Ryan, Fu, Daniel Y., Ré, Christopher, Mirhoseini, Azalia
Transformer-based large language models (LLMs) are now deployed to hundreds of millions of users. LLM inference is commonly performed on batches of sequences that share a prefix, such as few-shot examples or a chatbot system prompt. Decoding in this
Externí odkaz:
http://arxiv.org/abs/2402.05099
Publikováno v:
Annual Review of Biomedical Data Science; 2024, Vol. 7 Issue 1, p295-316, 19p
Autor:
Ehrlich, Ryan1 (AUTHOR), Kamga, Larisa2 (AUTHOR), Gil, Anna3 (AUTHOR), Luzuriaga, Katherine2 (AUTHOR), Selin, Liisa K.3 (AUTHOR), Ghersi, Dario1 (AUTHOR) dghersi@unomaha.edu
Publikováno v:
BMC Bioinformatics. 9/7/2021, Vol. 22 Issue 1, p1-14. 14p.
Autor:
Ehrlich, Ryan, Kamga, Larisa, Gil, Anna, Luzuriaga, Katherine, Selin, Liisa K., Ghersi, Dario
Additional file 1. Supplementary Figures and Tables.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86fd1fc50eb76bb7ee2971780342a920