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.

我们呈现了Open Pre-trained Transformers (OPT)，一个解码器型的预训练transformers套件，范围从125M到175B个参数，这些我们希望与感兴趣的研究人员完全和负责任地分享。 我们展示了OPT-175B与GPT-3相媲美，同时仅需要1/7的碳足迹进行开发。

OPT：开放预训练Transformer语言模型