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Model Details
Domain:
Model Access:
Open weights (non-commercial)
Citations:
4260
Introduction
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning. Given their computational cost, these models are difficult to replicate without significant capital. For the few that are available through APIs, no access is granted to the full model weights, making them difficult to study. We present Open Pre-trained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M to 175B parameters, which we aim to fully and responsibly share with interested researchers. We show that OPT-175B is comparable to GPT-3, while requiring only 1/7th the carbon footprint to develop. We are also releasing our logbook detailing the infrastructure challenges we faced, along with code for experimenting with all of the released models.
Benchmarking
FLOPs1.1e+23
Notes: OPT-66B was trained for 140k steps, using a batch size of 2M tokens (see the OPT baselines logbook and Table 1 in Zhang et al. (2022), respectively), so training took 140e3 ∗ 2e6 ∗ 66e9 ∗ 6 = 1.1e23 FLOP
Training
Training Code Accessibilitynon-commercial for weights: https://ai.meta.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/ training code (MIT) https://github.com/facebookresearch/metaseq/blob/main/docs/training.md
HardwareNVIDIA A100 SXM4 80 GB
Size Notes: "Our final corpus contains roughly 180B tokens."
Parameters
Parameters66000000000
Authors
Susan Zhang∗ , Stephen Roller∗ , Naman Goyal∗ , Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott† , Sam Shleifer† , Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, Luke Zettlemoyer
Multimodal
Vision