hybrid-diff-ar/olmo-data-160B

OLMoE-mix-0824, 160B-token subset (dolma2-tokenized)

A larger sibling of hybrid-diff-ar/olmo-data-50B (same sampling, same seed): every whole file there is also here, and its 7 partial (*.prefix*) files are replaced by longer extents.

A 160.0B-token, source-proportional subset of OLMoE-mix-0824, the pretraining mix of the standard OLMo2 model ladder in OLMo-core (≤ v2.4.0, src/scripts/train/OLMo2/OLMo2-ladder.py). It also includes the full v3-small perplexity validation sets used by that ladder. Everything is pre-tokenized with the allenai/dolma2-tokenizer and laid out exactly like http://olmo-data.org, so OLMo-core can read it with mix_base_dir / --data-root pointed at a local download of this repo.

160B tokens covers a 10×Chinchilla run of the 600M ladder model (119.48B tokens) in under one epoch.

Contents

split	files	tokens	size
train (preprocessed/)	50	160.000B	640.0 GB
validation (eval-data/perplexity/v3_small_dolma2-tokenizer/)	11	6.69M	26.7 MB

Train composition (identical to the full mix's token shares):

source	tokens	share
dclm	152.15B	95.10%
starcoder	3.43B	2.14%
pes2o	2.42B	1.51%
proofpile-2-arxiv	0.86B	0.54%
proofpile-2-open-web-math	0.50B	0.31%
proofpile-2-stack	0.49B	0.31%
wikipedia	0.15B	0.09%

Validation sets: c4_en, dolma_books, dolma_common-crawl, dolma_pes2o, dolma_reddit, dolma_stack, dolma_wiki, ice, m2d2_s2orc, pile, wikitext_103.

How the subset was made

Each source gets a token budget proportional to its share of the full mix (3.875T tokens, 1,117 files). Within a source, files are taken whole in a seeded random order (seed 34521), and the budget is topped off with a prefix of one more file. Those files are named *.prefix<NUM_BYTES>.npy. subset_plan.tsv lists every file with its original path and the number of bytes taken; train_files.txt lists the train files as label,path lines.

File format

Each .npy file is a raw uint32 token array with no NumPy header (the OLMo convention), i.e. read it with numpy.memmap(path, dtype=numpy.uint32, mode="r"). Documents are separated by the EOS token (id 100257).

Usage with OLMo-core

from olmo_core.data import NumpyFSLDatasetConfig, TokenizerConfig

root = "/path/to/olmo-data-160B"
paths = [f"{root}/{line.split(',', 1)[1].strip()}" for line in open(f"{root}/train_files.txt")]
dataset = NumpyFSLDatasetConfig(
    paths=paths, sequence_length=2048, tokenizer=TokenizerConfig.dolma2(), work_dir="/tmp/olmo-work"
)

For validation, use DataMix.v3_small_ppl_validation with mix_base_dir=root.

License and attribution

The data is a redistribution of a subset of AI2's OLMoE-mix-0824 (downloaded from http://olmo-data.org), released under the ODC-By 1.0 license. Each component keeps its original license and terms of use (e.g. DCLM, The Stack / StarCoder data, peS2o, Proof-Pile-2, Wikipedia); please consult them before use. Credit: Allen Institute for AI (OLMo / OLMoE).