Summary of Nemotron-4 15b Technical Report, by Jupinder Parmar and Shrimai Prabhumoye and Joseph Jennings and Mostofa Patwary and Sandeep Subramanian and Dan Su and Chen Zhu and Deepak Narayanan and Aastha Jhunjhunwala and Ayush Dattagupta and Vibhu Jawa and Jiwei Liu and Ameya Mahabaleshwarkar and Osvald Nitski and Annika Brundyn and James Maki and Miguel Martinez and Jiaxuan You and John Kamalu and Patrick Legresley and Denys Fridman and Jared Casper and Ashwath Aithal and Oleksii Kuchaiev and Mohammad Shoeybi and Jonathan Cohen and Bryan Catanzaro
Nemotron-4 15B Technical Report
by Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, Sandeep Subramanian, Dan Su, Chen Zhu, Deepak Narayanan, Aastha Jhunjhunwala, Ayush Dattagupta, Vibhu Jawa, Jiwei Liu, Ameya Mahabaleshwarkar, Osvald Nitski, Annika Brundyn, James Maki, Miguel Martinez, Jiaxuan You, John Kamalu, Patrick LeGresley, Denys Fridman, Jared Casper, Ashwath Aithal, Oleksii Kuchaiev, Mohammad Shoeybi, Jonathan Cohen, Bryan Catanzaro
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Nemotron-4 15B, a large multilingual language model with 15 billion parameters, trained on 8 trillion text tokens. The model demonstrates strong performance across various evaluation areas, outperforming existing open models in four out of seven areas and matching the leading open models in the remaining three. Specifically, Nemotron-4 15B showcases superior multilingual capabilities, even surpassing larger and specialized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Nemotron-4 15B is a super powerful language model that can understand many languages. It’s trained on a huge amount of text data and does really well in lots of tasks. This includes understanding English texts, texts in other languages, and even coding tasks. The model is so good at understanding different languages that it outperforms bigger models that are specifically designed for this task. |
Keywords
* Artificial intelligence * Language model
