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olmo-data-50B

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Text Generation
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pretraining
olmo
olmo-core
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metadata 
license: odc-by
pretty_name: OLMoE-mix-0824 50B-token subset (dolma2 tokenizer)
language:
  - en
task_categories:
  - text-generation
tags:
  - pretraining
  - olmo
  - olmo-core

OLMoE-mix-0824, 50B-token subset (dolma2-tokenized)

A 50.0B-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.

50B tokens covers a 10×Chinchilla run of the 190M ladder model (38.07B tokens) in under one epoch.

Contents

split files tokens size
train (preprocessed/) 21 50.000B 200.0 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 47.55B 95.10%
starcoder 1.07B 2.14%
pes2o 0.76B 1.51%
proofpile-2-arxiv 0.27B 0.54%
proofpile-2-open-web-math 0.16B 0.31%
proofpile-2-stack 0.15B 0.31%
wikipedia 0.05B 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-50B"
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).