Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Abdelwahab, Hammam"'
Autor:
Brandizzi, Nicolo', Abdelwahab, Hammam, Bhowmick, Anirban, Helmer, Lennard, Stein, Benny Jörg, Denisov, Pavel, Saleem, Qasid, Fromm, Michael, Ali, Mehdi, Rutmann, Richard, Naderi, Farzad, Agy, Mohamad Saif, Schwirjow, Alexander, Küch, Fabian, Hahn, Luzian, Ostendorff, Malte, Suarez, Pedro Ortiz, Rehm, Georg, Wegener, Dennis, Flores-Herr, Nicolas, Köhler, Joachim, Leveling, Johannes
This paper presents a comprehensive overview of the data preparation pipeline developed for the OpenGPT-X project, a large-scale initiative aimed at creating open and high-performance multilingual large language models (LLMs). The project goal is to
Externí odkaz:
http://arxiv.org/abs/2410.08800
Autor:
Ali, Mehdi, Fromm, Michael, Thellmann, Klaudia, Ebert, Jan, Weber, Alexander Arno, Rutmann, Richard, Jain, Charvi, Lübbering, Max, Steinigen, Daniel, Leveling, Johannes, Klug, Katrin, Buschhoff, Jasper Schulze, Jurkschat, Lena, Abdelwahab, Hammam, Stein, Benny Jörg, Sylla, Karl-Heinz, Denisov, Pavel, Brandizzi, Nicolo, Saleem, Qasid, Anirban, Bhowmick, John, Chelsea, Suarez, Pedro Ortiz, Ostendorff, Malte, Jude, Alex, Manjunath, Lalith, Weinbach, Samuel, Penke, Carolin, Asaadi, Shima, Barth, Fabio, Sifa, Rafet, Küch, Fabian, Jäkel, René, Rehm, Georg, Kesselheim, Stefan, Köhler, Joachim, Flores-Herr, Nicolas
We present preliminary results of the project OpenGPT-X. At present, the project has developed two multilingual LLMs designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset
Externí odkaz:
http://arxiv.org/abs/2410.03730
Autor:
Ali, Mehdi, Fromm, Michael, Thellmann, Klaudia, Rutmann, Richard, Lübbering, Max, Leveling, Johannes, Klug, Katrin, Ebert, Jan, Doll, Niclas, Buschhoff, Jasper Schulze, Jain, Charvi, Weber, Alexander Arno, Jurkschat, Lena, Abdelwahab, Hammam, John, Chelsea, Suarez, Pedro Ortiz, Ostendorff, Malte, Weinbach, Samuel, Sifa, Rafet, Kesselheim, Stefan, Flores-Herr, Nicolas
The recent success of Large Language Models (LLMs) has been predominantly driven by curating the training dataset composition, scaling of model architectures and dataset sizes and advancements in pretraining objectives, leaving tokenizer influence as
Externí odkaz:
http://arxiv.org/abs/2310.08754
Autor:
Abdelwahab, Hammam
Machine learning-based solutions are frequently adapted in several applications that require big data in operations. The performance of a model that is deployed into operations is subject to degradation due to unanticipated changes in the flow of inp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f30cffde6f12ef6e10020891f4b2fbf7