OPT

The Open Pre-trained Transformer (OPT) models are a collection of large language models with parameters ranging from 125 million to 175 billion. These models were trained to perform zero- and few-shot learning, which has demonstrated significant capabilities across various language tasks. OPT models are designed to be a more accessible alternative to other large-scale language models, such as GPT-3, which often requires substantial resources to replicate due to their computational costs. OPT also stands out for its smaller environmental footprint during development, requiring only one-seventh of the carbon footprint compared to GPT-3. Researchers behind OPT have taken care to share their models fully and responsibly, providing not only the model weights but also the logbook of their development challenges and the code necessary for experimentation.

Key Features

• Highly Capable Models: OPT models exhibit strong performance in zero- and few-shot learning tasks.

• Range of Sizes: The OPT suite offers a variety of model sizes, from 125M to 175B parameters.

• Accessible and Transparent: Full model weights and development details are shared with the research community.

• Eco-Friendly Development: OPT requires significantly less carbon footprint compared to models like GPT-3.

• Supportive Resources: The release includes a detailed logbook and code for researchers.

FAQ

What are Open Pre-trained Transformers (OPT)?

The Open Pre-trained Transformers (OPT) are a series of decoder-only pre-trained language models designed for various language tasks and are intended to be shared fully and responsibly with researchers.

What is the parameter range of OPT models?

OPT models range from 125 million to 175 billion parameters, catering to different research needs and computational capabilities.

What is special about the OPT-175B model?

OPT-175B, which is comparable to GPT-3, is one of the model sizes available and has been especially noted for its remarkable capabilities in zero- and few-shot learning.

Who are the authors of the OPT paper?

Researchers Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, and several others contributed to the development of the OPT models.

How do OPT models compare to GPT-3 in terms of environmental impact?

One of the goals for developing the OPT models was to create large language models with a lower environmental impact, and the OPT-175B model has been developed with just 1/7th the carbon footprint of GPT-3.