README.md

metadata

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 (sample-10BT config) prepared for byte-level language modeling, in the spirit of 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, 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

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).