arXiv:2204.06745cs.CL
GPT-NeoX-20B: An Open-Source Autoregressive Language Model
Abstract
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.
If you are interested in the infrastructure-level details of how similar models are trained by lesser known groups, take a look at this paper: https://arxiv.org/abs/2204.06745 Quotes from the paper: Our model is trained using a codebase that builds on Megatron (Shoeybi et al.…
kir-gadjelloView on HNParent context