GPT-1 Statistics: Stats Facts and Trends You Need to Know!

Disclosure (full version- https://nikolaroza.com/affiliate-disclosure/): Some of the links you’ll encounter are affiliate links. If you click and buy something, I may get a commission. Thank you! 

Looking for the latest GPT-1 statistics facts and trends for 2023?

My updated guide has everything you need to know, and more.

All the references and resources I used in crafting my GPT-1 stats guide are listed at the bottom of the page.

GPT-1 statistics facts and trends guide (September update)
GPT-1 statistics facts and trends guide (September update)

Key GPT-1 Statistics Facts and Trends


  • GPT-1 was released in 2018 by OpenAI as their first iteration of a language model using the Transformer architecture.
  • The architecture that supported GPT-1 while it was live is 12-level, 12-headed Transformer decoder (no encoder), followed by linear-softmax.
  • While it was live GPT-1 had 120 million parameters. This pales in comparison to GPT-2 which had 1.5 billion parameters and GPT-3 which had 175 billion parameters.
  • GPT-1 was trained on BookCorpus, which is a 4.5 GB of text compromising of 7000 unpublished books of various genres. OpenAI picked BookCorpus as a perfect training dataset because the long passages of continuous text in the unpublished volumes of fiction helped GPT-1 handle better long-range information.
  • GPT-1 was not trained using RLHF technique.
  • CommonCrawl was not used to train GPT-1.
  • The cost of training GPT-1 is currently unknown and undisclosed by OpenAI.

What Is GPT-1?

GPT-1 (Generative Pre-trained Transformer 2) was a large language model built by OpenAI (learn about OpenAI in my OpenAI statistics post) and the first in their foundational series of GPT models (GPT-1, GPT-2, GPT-3, ChatGPT built on GPT-3.5 and GPT-4).

The existence of GPT-1 language model was made possible following Google’s invention of the transformer architecture in 2017.

When Did OpenAI Launch GPT-1?

GPT-1 was released in 2018 by OpenAI as their first iteration of a language model using the Transformer architecture.

The existence of GPT-1 language model was made possible following Google’s invention of the transformer architecture in 2017.

What Architecture Supported GPT-1 While the Model Was Live?

The architecture that supported GPT-1 while it was live is 12-level, 12-headed Transformer decoder (no encoder), followed by linear-softmax.

How Many Parameters Did GPT-1 Have While It Was Live?

While it was live GPT-1 had 120 million parameters. This pales in comparison to GPT-2 which had 1.5 billion parameters and GPT-3 which had 175 billion parameters.

Learn about GPT-2 and GPT-3 by reading my GPT-2 statistics and GPT-3 statistics guides next.

OpenAI keeps a secret the number of parameters of GPT-4, but estimates vary between 200 billion to 220 billion parameters.

Learn about GPT-4 by reading my GPT-4 statistics guide next.

What Data Was GPT-1 Trained on?

GPT-1 was trained on BookCorpus, which is a 4.5 GB of text compromising of 7000 unpublished books of various genres.

OpenAI picked BookCorpus as a perfect training dataset because the long passages of continuous text in the unpublished volumes of fiction helped GPT-1 handle better long-range information.

Other datasets were considered but ultimately rejected due to long strings of polluted text (gibberish sentences).

architecture parameter count training data
GPT-3GPT-2, but with modification to allow larger scaling. 175 billion 570 GB plaintext, 0.4 trillion tokens. Mostly CommonCrawl, WebText, English Wikipedia, and two books corpora (Books1 and Books2).
GPT-2GPT-1, but with modified normalization 1.5 billion WebText: 40 GB of text, 8 million documents, from 45 million webpages upvoted on Reddit.
GPT-112-level, 12-headed Transformer decoder (no encoder), followed by linear-softmax. 0.12 billion BookCorpus: 4.5 GB of text, from 7000 unpublished books of various genres.

Was GPT-1 Trained Using the RLHF Technique?

GPT-1 was not trained using RLHF technique. RLHF (Reinforcement Learning from Human Feedback) was used to train GPT-3, ChatGPT and GPT-4 language models.

Who Can use GPT-1?

Currently, no one can use GPT-1 given that OpenAI retired that model a long time ago.

Was CommonCrawl Used to Train GPT-1?

CommonCrawl was not used to train GPT-1.

CommonCrawl was used to train GPT-3, ChatGPT and GPT-4.

Was GPT 1 Able To Generate AI Images Similar to DALL-E?

GPT-1 was a primitive LLM barely able to string a few coherent sentences together. It was not able to generate AI imagery similar to what OpenAI DALL-E can do.

Learn about the DALL-E AI image generator by reading my DALL-E stats post next.

Are There Any AI Writing Tools Using GPT-1?

Currently, there are no AI writing tools using GPT-1. It’s an obsolete language model whose purpose was more to showcase the bright future ahead than to generate any meaningful volume of textual content.

GPT-1 Stats Facts and Trends Guide (Conclusion)


My updated guide lists the best and latest GPT-1 statistics facts and trends for 2023.

I hope you enjoyed it because the guide is now over.

During my research, I consulted these resources below:

References:

  • (https://en.wikipedia.org/wiki/GPT-1)
  • (https://www.makeuseof.com/gpt-models-explained-and-compared/)
  • GPT-1, GPT-2 and GPT-3 models explained
    (https://360digitmg.com/blog/types-of-gpt-in-artificial-intelligence)
  • The Journey of Open AI GPT models
    (https://medium.com/walmartglobaltech/the-journey-of-open-ai-gpt-models-32d95b7b7fb2)

 

Nikola Roza

Nikola Roza is an affiliate marketer and blogger behind Nikola Roza- SEO for the Poor and Determined. He writes for bloggers who don't have huge marketing budgets and who still want to carve out a niche online and a better life for themselves. He's also passionate about precious metals IRAs and how to invest in gold and silver for a safer financial future. Learn about Nikola here, or read his blog posts and guides here.

Add a Comment

Your email address will not be published. Required fields are marked *