---
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`](https://huggingface.co/datasets/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:

```python
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")
```