The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text

Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic, Stella Biderman, Baber Abbasi, Luca Soldaini, Enrico Shippole, A. Feder Cooper, Aviya Skowron, Shayne Longpre, Lintang Sutawika, Alon Albalak, Zhenlin Xu, Guilherme Penedo, Loubna Ben allal, Elie Bakouch, John David Pressman, Honglu Fan, Dashiell Stander et al. (7 additional authors not shown)

Keywords: Language model pre-training data, openly licensed data

TL;DR: We collect an 8 TB of public domain and openly licensed text and use it to pre-train a performant 7B-parameter LLM.

Abstract: Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement and ethical concerns. Training LLMs on openly licensed text presents a first step towards addressing these issues, but prior data collection efforts have yielded datasets too small or low-quality to produce performant LLMs. To address this gap, we collect, curate, and release the Common Pile v0.1, an eight terabyte collection of openly licensed text designed for LLM pretraining. The Common Pile comprises content from 30 sources that span diverse domains including research papers, code, books, encyclopedias, educational materials, audio transcripts, and more. Crucially, we validate our efforts by training two 7 billion parameter LLMs on text from the Common Pile: Comma v0.1-1T and Comma v0.1-2T, trained on 1 and 2 trillion tokens respectively. Both models attain competitive performance to LLMs trained on unlicensed text with similar computational budgets, such as Llama 1 and 2 7B. In addition to releasing the Common Pile v0.1 itself, we also release the code used in its creation as well as the training mixture and checkpoints for the Comma v0.1 models.

Dataset URL: https://huggingface.co/collections/common-pile/common-pile-v01-6826b454a5a6a445d0b51b37

Code URL: https://github.com/r-three/common-pile/tree/main

Primary Area: Datasets & Benchmarks for applications in language modeling and vision language modeling

Submission Number: 2139