GPT-NeoX-20B: An Open-Source Autoregressive Language Model
| Autor: | Black, Sid, Biderman, Stella, Hallahan, Eric, Anthony, Quentin, Gao, Leo, Golding, Laurence, He, Horace, Leahy, Connor, McDonell, Kyle, Phang, Jason, Pieler, Michael, Prashanth, USVSN Sai, Purohit, Shivanshu, Reynolds, Laria, Tow, Jonathan, Wang, Ben, Weinbach, Samuel |
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| Rok vydání: | 2022 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of submission. In this work, we describe \model{}'s architecture and training and evaluate its performance on a range of language-understanding, mathematics, and knowledge-based tasks. We find that GPT-NeoX-20B is a particularly powerful few-shot reasoner and gains far more in performance when evaluated five-shot than similarly sized GPT-3 and FairSeq models. We open-source the training and evaluation code, as well as the model weights, at https://github.com/EleutherAI/gpt-neox. Comment: To appear in the Proceedings of the ACL Workshop on Challenges & Perspectives in Creating Large Language Models |
| Databáze: | arXiv |
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