metadata
pretty_name: github-code-nanochatbpe-1B
language:
- en
tags:
- pretokenized
- language-modeling
size_categories:
- 1B<n<10B
github-code-nanochatbpe-1B
GitHub Code (all-all) (from codeparrot/github-code), pre-tokenized with the nanochatbpe tokenizer (vocab 65,536) and packaged as flat uint16 token-id .bin files for fast memmap training.
| file | split | tokens |
|---|---|---|
train.bin |
train | 1,000,000,000 |
val.bin |
val | 10,000,000 |
train and val are disjoint held-out partitions. Each .bin is a raw little-endian uint16 stream (no header); token count = filesize / 2, and train.meta.json / val.meta.json carry the full metadata. The tokenizer/ files (when present) are the exact tokenizer used to produce these ids.
Load a bin with the standard Hugging Face downloader:
from huggingface_hub import hf_hub_download
import numpy as np
path = hf_hub_download(repo_id="alexkstern/github-code-nanochatbpe-1B", filename="train.bin", repo_type="dataset")
tokens = np.memmap(path, dtype="uint16", mode="r")