Year: 2024
Type: preprint
Abstract: We introduce Nemotron-4 15B, a 15-billion-parameter large multilingual language model trained on 8 trillion text tokens. Nemotron-4 15B demonstrates strong performance when assessed on English, multilingual, and coding tasks: it outperforms all existing similarly-sized open models on 4 out of 7 downstream evaluation areas and achieves competitive performance to the leading open models in the remaining ones. Specifically, Nemotron-4 15B exhibits the best multilingual capabilities of all similarly-sized models, even outperforming models over four times larger and those explicitly specialized for multilingual tasks.
Source: arXiv (Cornell University)
Authors: Parmar, Jupinder, Prabhumoye, Shrimai, Jennings, Joseph, Patwary, Mostofa, Subramanian, Sandeep, Su, Dan, Zhu, Chen, Narayanan, Deepak, Jhunjhunwala, Aastha, Dattagupta, Ayush, Jawa, Vibhu, Liu, Jiwei, Mahabaleshwarkar, Ameya, Nitski, Osvald, Brundyn, Annika, Maki, James, Martinez, Miguel, You, Jiaxuan, Kamalu, John, LeGresley, Patrick, Fridman, Denys, Casper, Jared, Aithal, Ashwath, Kuchaiev, Oleksii, Shoeybi, Mohammad, Cohen, Jonathan, Catanzaro, Bryan
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4392341317