OPT: Open Pre-trained Transformer Language Models

TL;DR

The paper introduces OPT, a suite of open-source pre-trained transformer models from 125M to 175B parameters, designed to democratize access and facilitate research while reducing environmental impact.

Contribution

It provides a large, open collection of transformer models with detailed documentation and tools, enabling broader research and responsible sharing of powerful language models.

Findings

OPT-175B matches GPT-3 performance

Requires only 1/7th of GPT-3's carbon footprint

Full suite and tools are openly available

Abstract

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.