metadata

license: odc-by
viewer: true
task_categories:
  - text-generation
language:
  - en
tags:
  - language-modeling
  - casual-lm
  - llm
pretty_name: Dolma
size_categories:
  - n>1T

Dolma

Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.

More information:

Read Dolma manuscript and its Data Sheet on ArXiv;
Explore the open source tools we created to curate Dolma.
Want to request removal of personal data? Use this form to notify us of documents containing PII about a specific user.

To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our GitHub project page!

2024-04-15: License Change. We have updated the license of Dolma to ODC-BY. Please see this blog post for more information.

Versions

At the moment, there are five versions of Dolma available:

Version	Default?	Release Date	Size (gzip)	Description
`v1_6`	✅	2024-01-31	5.4 TB	The latest version of Dolma, with 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.
`v1_6-sample`		2024-01-31	16.4 GB	A smaller sample of Dolma, with roughly 10 billion tokens. Useful for data exploration.
`v1_5`		2023-10-31	6.4 TB	The version of Dolma used to train OLMo-1B. Roughly 3 trillion tokens.
`v1_5-sample`		2023-10-31	2.9 TB	A sample of roughly 1.9 trillion tokens used to train OLMo-7B
`v1`		2023-08-18	6.0 TB	The first version of Dolma.

(Size difference between v1_6 and previous version is due to different set of metadata included in files: we removed redundant metadata in v1_6.)

Summary Statistics (v1.6)

Source	Doc Type	UTF-8 bytes (GB)	Documents (millions)	Unicode words (billions)	Llama tokens (billions)
Common Crawl	web pages	9,022	3,370	1,775	2,281
The Stack	code	1,043	210	260	411
C4	web pages	790	364	153	198
Reddit	social media	339	377	72	89
PeS2o	STEM papers	268	38.8	50	70
Project Gutenberg	books	20.4	0.056	4.0	6.0
Wikipedia, Wikibooks	encyclopedic	16.2	6.2	3.7	4.3
Total		11,519	4,367	2,318	3,059

Download

The fastest way to download Dolma is to clone this repository and use the files in the url directory. We recommend using wget in parallel mode to download the files. For example:

DATA_DIR="<path_to_your_data_directory>"
PARALLEL_DOWNLOADS="<number_of_parallel_downloads>"
DOLMA_VERSION="<version_of_dolma_to_download>"

git clone https://huggingface.co/datasets/allenai/dolma
mkdir -p "${DATA_DIR}"


cat "dolma/urls/${DOLMA_VERSION}.txt" | xargs -n 1 -P "${PARALLEL_DOWNLOADS}" wget -q -P "$DATA_DIR"

Then, to load this data using HuggingFace's datasets library, you can use the following code:

import os
from datasets import load_dataset

os.environ["DATA_DIR"] = "<path_to_your_data_directory>"
dataset = load_dataset("allenai/dolma", split="train")

Licensing Information

We are releasing this dataset under the terms of ODC-BY. By using this dataset, you are also bound any license agreements and terms of use of the original data sources.

Bibtex

If you use our dataset or tooling, please cite us at:

@article{dolma,
  title = {{Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
  author={
    Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
    Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
    Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and
    Nathan Lambert and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and
    Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and
    Emma Strubell and Nishant Subramani and Oyvind Tafjord and Pete Walsh and Luke Zettlemoyer and
    Noah A. Smith and Hannaneh Hajishirzi and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
  },
  year = {2024},
  journal={arXiv preprint},
}