| 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") | |
| ``` | |