EleutherAI, an AI research organization, has released what it claims is one of the largest collections of licensed and open-domain text for training AI models.
The dataset, called the Common Pile v0.1, took around two years to complete in collaboration with AI startups Poolside, Hugging Face, and others, along with several academic institutions. Weighing in at 8 terabytes in size, the Common Pile v0.1 was used to train two new AI models from EleutherAI, Comma v0.1-1T and Comma v0.1-2T, that EleutherAI claims perform on par with models developed using unlicensed, copyrighted data.
AI companies, including OpenAI, are embroiled in lawsuits over their AI training practices, which rely on scraping the web — including copyrighted material like books and research journals — to build model training datasets. While some AI companies have licensing arrangements in place with certain content providers, most maintain that the U.S. legal doctrine of fair use shields them from liability in cases where they trained on copyrighted work without permission.
EleutherAI argues that these lawsuits have “drastically decreased” transparency from AI companies, which the organization says has harmed the broader AI research field by making it more difficult to understand how models work and what their flaws might be.
“[Copyright] lawsuits have not meaningfully changed data sourcing practices in [model] training, but they have drastically decreased the transparency companies engage in,” Stella Biderman, EleutherAI’s executive director, wrote in a blog post on Hugging Face early Friday. “Researchers at some companies we have spoken to have also specifically cited lawsuits as the reason why they’ve been unable to release the research they’re doing in highly data-centric areas.”
The Common Pile v0.1, which can be downloaded from Hugging Face’s AI dev platform and GitHub, was created in consultation with legal experts, and it draws on sources, including 300,000 public domain books digitized by the Library of Congress and the Internet Archive. EleutherAI also used Whisper, OpenAI’s open source speech-to-text model, to transcribe audio content.
EleutherAI claims Comma v0.1-1T and Comma v0.1-2T are evidence that the Common Pile v0.1 was curated carefully enough to enable developers to build models competitive with proprietary alternatives. According to EleutherAI, the models, both of which are 7 billion parameters in size and were trained on only a fraction of the Common Pile v0.1, rival models like Meta’s first Llama AI model on benchmarks for coding, image understanding, and math.
Parameters, sometimes referred to as weights, are the internal components of an AI model that guide its behavior and answers.
“In general, we think that the common idea that unlicensed text drives performance is unjustified,” Biderman wrote in her post. “As the amount of accessible openly licensed and public domain data grows, we can expect the quality of models trained on openly licensed content to improve.”
The Common Pile v0.1 appears to be in part an effort to right EleutherAI’s historical wrongs. Years ago, the company released The Pile, an open collection of training text that includes copyrighted material. AI companies have come under fire — and legal pressure — for using The Pile to train models.
EleutherAI is committing to releasing open datasets more frequently going forward in collaboration with its research and infrastructure partners.
Updated 9:48 a.m. Pacific: Biderman clarified in a post on X that EleutherAI contributed to the release of the datasets and models, but that their development involved many partners, including the University of Toronto, which helped lead the research.
