---
license: other
language:
  - en
task_categories:
  - text-generation
tags:
  - pretraining
  - nanochat
  - rsm
  - pretokenized
  - nemotron
size_categories:
  - 10M<n<100M
---

# Nemotron Specialized 100B — Nanochat packed

Training-ready, pretokenized shards derived from
[`nvidia/Nemotron-Pretraining-Specialized-v1`](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1)
at revision `9ed3718b5f2ae29074c5e34e64115432b7c4320f`.

The data was selected, packed, and deterministically shuffled for Nanochat
language-model pretraining. It is the exact corpus used by the matched AR,
MTP-4, and RSM experiments in
[`hanseungwook/rsm-llm`](https://github.com/hanseungwook/rsm-llm).

## Contents

| Split | Segments | Sequences | Training tokens |
| --- | --- | ---: | ---: |
| `train_50b` | `train_segment_000` | 24,254,720 | 49,673,666,560 |
| `train_100b` | `train_segment_000` + `train_segment_001` | 48,509,440 | 99,347,333,120 |
| `validation` | six source-specific shards | 12,288 | 25,165,824 |

Each training row contains 2,049 little-endian `uint16` token IDs: a
2,048-token context plus the final shifted target. Training shards are already
globally shuffled. `manifest.json` records every shard path, size, SHA-256
digest, source mixture, preprocessing seed, tokenizer revision, and named
split. The small `.source.bin` companions hold one source ID per packed row.

The included `tokenizer/` directory is the pinned artifact from
[`karpathy/nanochat-d32`](https://huggingface.co/karpathy/nanochat-d32) at
revision `016dba034c9c0ca9033ad1bc721bceff54680600`.

## Download

The complete repository is about 199 GB. `snapshot_download` is resumable.

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="hanseungwook/nemotron-specialized-100b-packed",
    repo_type="dataset",
    local_dir="/path/to/nemotron-specialized-100b-packed",
)
```

To download only the 50B training split, keep segment 0 and the supporting
metadata:

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="hanseungwook/nemotron-specialized-100b-packed",
    repo_type="dataset",
    local_dir="/path/to/nemotron-specialized-100b-packed",
    allow_patterns=[
        "manifest.json",
        "provenance/*",
        "tokenizer/*",
        "train_segment_000/*",
        "validation/*/*",
        "verification/*",
    ],
)
```

## Train with RSM-LLM

From a checkout of `hanseungwook/rsm-llm`, set `DATA_DIR` to the downloaded
snapshot and launch a training variant. For example, this runs the 100B RSM
configuration:

```bash
export DATA_DIR=/path/to/nemotron-specialized-100b-packed
export NANOCHAT_BASE_DIR=/path/to/nanochat-runtime

DATASET_MANIFEST="$DATA_DIR/manifest.json" \
TOKENIZER_DIR="$DATA_DIR/tokenizer" \
DATASET_SPLIT=train_100b \
bash runs/nemotron_mtp_baselines.sh rsm
```

The launcher also accepts `ar`, `mtp`, `rsm-gamma`, and `rsm-gamma-film`.
Use `DATA_CACHE_DIR` to copy active shards to node-local storage during
training.

## Integrity and provenance

The completed local verification pass checked all 194 manifest-referenced
binary files (198,890,550,784 bytes). The verification record is included at
`verification/complete.json`; full construction metadata is under
`provenance/`. Consumers can validate the portable manifest with
`nanochat.packed_data.load_manifest` and use the shard digests in
`manifest.json` for a full byte-level audit.

## License and attribution

This is a format conversion and deterministic mixture of the NVIDIA dataset;
it does not replace or narrow any upstream terms. Most source samples are
CC BY 4.0. The upstream card states that Wiki-Rewrite is CC BY-SA 4.0 and
Scientific-Coding is GFDL, and it identifies generator-model licenses that may
apply to downstream models. The repository is therefore tagged
`license: other`.

Review the
[`nvidia/Nemotron-Pretraining-Specialized-v1` dataset card](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-Specialized-v1)
before redistributing this data or a trained model, and retain NVIDIA's
attribution. If you use the data in research, cite the NVIDIA Nemotron 3 Nano
technical report listed on the upstream card.