# Dataset Guide

This folder documents how the 20 GiB JSONL checkpoints are generated, validated,
uploaded, and consumed for training.

## Design Goal

The dataset is optimized for code completion, FIM training, and architecture
ablation between Dense and MoE models. Each checkpoint is a self-contained unit
that can be uploaded to Google Drive, Hugging Face, or mounted in Colab.

## Generation Method

Generation must be streaming and out-of-core:

- Never load a whole corpus or checkpoint into RAM.
- Write JSONL shards incrementally.
- Use a disk-backed dedup index.
- Keep source files immutable.
- Stop generation when disk free space approaches the safety floor.

## Checkpoint Unit

Each checkpoint targets about 20 GiB because that size is practical for Google
Drive uploads and Colab/H100 training runs. A checkpoint owns its local JSONL
files; files are moved into the checkpoint folder rather than copied.

Checkpoint folders are intentionally dataset-only:

```text
dataset/
  checkpoint_YYYYMMDD_HHMMSS_bundleNN_20g/
    dataset/
      *.jsonl
```

Reports and checksums live outside checkpoint folders at
`dataset_guide/checkpoint_reports/<checkpoint>/`.

## Required Validation

Before a checkpoint is considered upload-ready:

- Every line must parse as JSON.
- Every record must contain non-empty `text`.
- In-bundle duplicate count must be zero.
- Checksums must be regenerated after any file rewrite.
- `UPLOAD_READY.md` in `checkpoint_reports/<checkpoint>/` must say the
  checkpoint is ready.

## Training Loader Expectations

Training loaders should read `dataset/*.jsonl` line by line. They should append
EOS between records, preserve FIM tokens, and avoid multi-worker duplication by
sharding files or line ranges across workers.