README.md

---
pretty_name: OLMo 3 Pre-tokenized and Processed Training Data
license: odc-by
viewer: false
task_categories:
- text-generation
tags:
- text
- olmo
- pretokenized
- language-modeling
- post-training
- sft
---

# OLMo 3 pre-tokenized and processed training data

**This repository redistributes Ai2/AllenAI's official OLMo 3 training data in an OLMo-core-ready archive layout; it is not a newly curated mixture.**

This is an archive distribution, not a row-based Hugging Face Datasets builder; download and extract it instead of using `datasets.load_dataset()` or the Dataset Viewer. Its main payload is `uint32` token-ID streams, with aligned label masks for the post-training stages. Extraction reconstructs a data-root directory that the corresponding OLMo-core training recipes can load directly.

All token-ID streams use the 100,278-entry OLMo 3 tokenizer family. Stages 1-3 and Eval use [`allenai/dolma2-tokenizer`](https://huggingface.co/allenai/dolma2-tokenizer); [OLMo-core documents](https://github.com/allenai/OLMo-core/blob/14b15cc2d836a37bc47b4824ed6b542b39a2632c/src/olmo_core/data/mixes/__init__.py#L92-L104) that the `allenai/dolma3-tokenizer` name in the Stage 2/3 paths is the same tokenizer. Stages 4-5 use [`allenai/olmo-3-tokenizer-instruct-dev@55f211d`](https://huggingface.co/allenai/olmo-3-tokenizer-instruct-dev/tree/55f211dfda3974963b869e490617447045069a64), see details below.

## Layout

Layout of this Hugging Face repository:

```text
.
├── README.md
├── extract.sh
├── archives/
│   ├── stage1/*.tar.zst
│   ├── stage2/*.tar.zst
│   ├── stage3/*.tar.zst
│   ├── stage4/*.tar.zst
│   ├── stage5/*.tar.zst
│   └── eval/*.tar.zst
└── lists/
    ├── stage1/*.files
    ├── stage2/*.files
    ├── stage3/*.files
    ├── stage4/*.files
    ├── stage5/*.files
    └── eval/*.files
```

The `.files` lists document each archive's members and are not required for extraction.

Extract with

```bash
bash ./extract.sh /path/to/olmo3_data_root
```

Layout after extraction:

```text
/path/to/olmo3_data_root/
├── preprocessed/
│   ├── dolma2-0625/v0.1-150b/allenai/dolma2-tokenizer/...  # Stage 1
│   ├── dolma3-dolmino-official/100B/allenai/dolma3-tokenizer/...  # Stage 2
│   └── dolma3_longmino_0625/allenai/dolma3-tokenizer/...  # Stage 3
├── Dolci-Think-SFT-7B/...  # Stage 4
├── Dolci-Instruct-SFT/...  # Stage 5
└── eval-data/
    └── perplexity/v3_small_dolma2-tokenizer/...  # Eval (Stages 1 and 2)
```

## Download

```bash
pip install -U huggingface_hub

repo_id=ArchSpace-Collection/OLMo3-1B-Dataset
hf download "$repo_id" --repo-type dataset --local-dir ./olmo3-hf
```

For a single training stage, download only the required archive groups plus this README and the extraction script. Stage 1 and Stage 2 configurations also use the perplexity-evaluation data:

```bash
hf download "$repo_id" --repo-type dataset --local-dir ./olmo3-hf \
  --include README.md extract.sh 'archives/stage1/*' 'archives/eval/*'
# modify `stage1` for other stages
```

## Extract into an OLMo data root

```bash
bash ./olmo3-hf/extract.sh /path/to/olmo3_data_root
```

Use `/path/to/olmo3_data_root` as the OLMo-core data root. Downloading and extracting only one stage creates just that stage's branches of the extracted layout shown above; later runs can safely merge additional groups into the same root.

## Official source and redistribution provenance

Stages 1-3 and Eval directly redistribute the official pre-tokenized files selected by the pinned manifests below, without retokenization or content transformation. The original corpus come from the official
[OLMo 3 pre-training artifacts](https://huggingface.co/collections/allenai/olmo-3-pre-training).

Stages 4-5 derive from the official [OLMo 3 post-training artifacts](https://huggingface.co/collections/allenai/olmo-3-post-training).

| Group | Extracted data | Upstream provenance | Exact OLMo-core source manifest |
|---|---|---|---|
| Stage 1 | `preprocessed/dolma2-0625/v0.1-150b/...` | [Dolma 3 Sample: 150B Mix](https://huggingface.co/datasets/allenai/dolma3_mix-150B-1025) | [`OLMo-mix-0625-150Bsample.txt`](https://github.com/allenai/OLMo-core/blob/cdb79229094da5075c4d4f5aa3bf5e392aeddb48/src/olmo_core/data/mixes/OLMo-mix-0625-150Bsample.txt) |
| Stage 2 | `preprocessed/dolma3-dolmino-official/100B/...` | [Dolma 3 Dolmino Mix: 100B](https://huggingface.co/datasets/allenai/dolma3_dolmino_mix-100B-1025) | [`OLMo-midtraining-mix-0625-100B.txt`](https://github.com/allenai/OLMo-core/blob/6b20747ac915025bd3340a4c991a8f2acf5824fd/src/olmo_core/data/mixes/OLMo-midtraining-mix-0625-100B.txt) |
| Stage 3 | `preprocessed/dolma3_longmino_0625/...` | [Dolma 3 Longmino Mix: 50B](https://huggingface.co/datasets/allenai/dolma3_longmino_mix-50B-1025) | [`OLMo-longmino-mix-0625.txt`](https://github.com/allenai/OLMo-core/blob/20548a05bacde64fa2116756b5cc763b34c60d7b/src/olmo_core/data/mixes/OLMo-longmino-mix-0625.txt) |
| Eval | `eval-data/perplexity/v3_small_dolma2-tokenizer/...` | OLMo-core multi-source PPL validation bundle; not a training mix or a single upstream dataset | [`v3-small-ppl-validation.txt`](https://github.com/allenai/OLMo-core/blob/cdb79229094da5075c4d4f5aa3bf5e392aeddb48/src/olmo_core/data/mixes/v3-small-ppl-validation.txt) |

The Dolci groups have official dataset cards rather than OLMo-core text manifests:

| Group | Extracted data | Official upstream dataset | Purpose |
|---|---|---|---|
| Stage 4 | `Dolci-Think-SFT-7B/...` | [Dolci-Think-SFT-7B](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B) | Processed SFT data for [Olmo-3-7B-Think-SFT](https://huggingface.co/allenai/Olmo-3-7B-Think-SFT) |
| Stage 5 | `Dolci-Instruct-SFT/...` | [Dolci-Instruct-SFT](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT) | Processed SFT mixture used for [Olmo-3-7B-Instruct-SFT](https://huggingface.co/allenai/Olmo-3-7B-Instruct-SFT) |

## Stage 4/5 preparation

Stages 4 and 5 use Open-Instruct's [`convert_sft_data_for_olmocore.py`](https://github.com/allenai/open-instruct/blob/5fb2acc161b572201628d50cc7145d109edac140/scripts/data/convert_sft_data_for_olmocore.py) with [`allenai/olmo-3-tokenizer-instruct-dev@55f211d`](https://huggingface.co/allenai/olmo-3-tokenizer-instruct-dev/tree/55f211dfda3974963b869e490617447045069a64).

This tokenizer choice follows [Open-Instruct's official guidance](https://github.com/allenai/open-instruct/blob/5fb2acc161b572201628d50cc7145d109edac140/docs/olmo3.md#tokenizer-settings) for both stages: Stage 5 uses the tokenizer as recommended, and Stage 4 uses the same Instruct template for Think SFT to prevent `<think>` spans from being incorrectly masked.

The following script runs both conversions from an installed Open-Instruct checkout:

```bash
#!/usr/bin/env bash
set -euo pipefail

open_instruct_dir=/path/to/open-instruct
output_root=/path/to/olmo3_data_root
cache_dir=/path/to/open-instruct-cache
tokenizer=allenai/olmo-3-tokenizer-instruct-dev
revision=55f211dfda3974963b869e490617447045069a64

cd "$open_instruct_dir"
for dataset in Dolci-Think-SFT-7B Dolci-Instruct-SFT; do
  python scripts/data/convert_sft_data_for_olmocore.py \
    --dataset_mixer_list "allenai/$dataset" 1.0 \
    --tokenizer_name_or_path "$tokenizer" \
    --tokenizer_revision "$revision" \
    --output_dir "$output_root/$dataset" \
    --dataset_local_cache_dir "$cache_dir" \
    --max_seq_length 32768 \
    --shuffle_seed 42 \
    --resume True
done
```

## File Size

| Archive group | Archives | Files | Raw size | Compressed size | Compressed / raw |
|---|---:|---:|---:|---:|---:|
| Stage 1 | 15 | 6,915 | 629.875 GB | 71.065 GB | 11.282% |
| Stage 2 | 18 | 11,933 | 399.802 GB | 83.303 GB | 20.836% |
| Stage 3 | 10 | 998 | 200.001 GB | 47.409 GB | 23.705% |
| Stage 4 | 4 | 284 | 123.519 GB | 17.268 GB | 13.980% |
| Stage 5 | 1 | 26 | 9.058 GB | 1.166 GB | 12.875% |
| Eval | 1 | 11 | 0.027 GB | 0.008 GB | 30.627% |
| **Total** | **49** | **20,167** | **1,362.282 GB** | **220.219 GB** | **16.165%** |

## License

This redistribution is made available under the same [Open Data Commons Attribution License v1.0 (ODC-By)](https://opendatacommons.org/licenses/by/1-0/) as the official OLMo 3 datasets. Credit for the underlying datasets belongs to Ai2/AllenAI and Team OLMo; downstream redistribution and use must preserve the attribution and notices required by ODC-By. See the official dataset cards linked above and Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use).