| --- |
| license: odc-by |
| language: |
| - en |
| size_categories: |
| - 10B<n<100B |
| tags: |
| - byte-level |
| - language-modeling |
| - pretraining |
| --- |
| |
| # FineWeb-Edu as raw bytes (sample-10BT) |
|
|
| [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) |
| (`sample-10BT` config) prepared for **byte-level language modeling**, in the |
| spirit of [nanoGPT](https://github.com/karpathy/nanoGPT)'s `train.bin` / |
| `val.bin` — except here there is no tokenizer at all: **one uint8 per UTF-8 |
| byte of text**. ~45 GB total. |
|
|
| Made for training tokenizer-free models (byte-level Transformers, |
| [H-Nets](https://arxiv.org/abs/2507.07955), etc.) and comparing them against |
| BPE baselines on identical data. |
|
|
| ## Format |
|
|
| Flat binary shards of `uint8`, directly memmap-able: |
|
|
| ``` |
| train_00000.bin ... train_000NN.bin (500 MB each) |
| val_00000.bin |
| meta.json |
| ``` |
|
|
| - Bytes `0..253`: raw UTF-8 bytes of the text |
| - Byte `254`: `<|doc|>` document separator (0xFE never occurs in valid UTF-8). |
| Every document **starts** with one. |
| - Byte `255`: reserved, never occurs |
| - Every 1000th document went to the `val` split; exact document and byte |
| counts are in `meta.json`. |
|
|
| ## Usage |
|
|
| ```python |
| import numpy as np |
| |
| shard = np.memmap("train_00000.bin", dtype=np.uint8, mode="r") |
| |
| # random crop as training example |
| x = shard[1_000_000 : 1_000_000 + 8192] |
| |
| # decode back to text |
| docs = bytes(shard[:100_000]).split(b"\xfe") |
| print(docs[1].decode("utf-8", errors="replace")[:200]) |
| ``` |
|
|
| ## Provenance |
|
|
| Produced by streaming `HuggingFaceFW/fineweb-edu` `sample-10BT` in order, |
| encoding each `text` field as UTF-8, and concatenating with separators. No |
| filtering, shuffling, or other processing was applied (occurrences of bytes |
| 0xFE/0xFF, impossible in valid UTF-8, would be stripped — in practice there |
| are none). License follows the source dataset (ODC-By, subject to |
| CommonCrawl ToU). |
|
|
