| # Dataset Guide |
|
|
| This folder documents how the 20 GiB JSONL checkpoints are generated, validated, |
| uploaded, and consumed for training. The current long-term target is 400GB total |
| mirrored to Hugging Face and Google Drive. |
|
|
| ## 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 staging JSONL parts incrementally. |
| - Merge selected staging parts into one checkpoint JSONL by streaming bytes, then |
| delete each staging part. |
| - 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. The preferred checkpoint shape is |
| one complete JSONL file per checkpoint. |
|
|
| Checkpoint folders are intentionally dataset-only: |
|
|
| ```text |
| dataset/ |
| checkpoint_YYYYMMDD_HHMMSS_bundleNN_20g/ |
| dataset/ |
| checkpoint_YYYYMMDD_HHMMSS_bundleNN_20g.jsonl |
| ``` |
|
|
| Reports and checksums live outside checkpoint folders at |
| `dataset_guide/checkpoint_reports/<checkpoint>/`. |
|
|
| Older legacy checkpoints may contain multiple 1 GiB JSONL parts until they are |
| repacked. New checkpoints should use the single-file layout above. |
|
|
| ## 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 the checkpoint JSONL line by line. They should |
| append EOS between records, preserve FIM tokens, and avoid multi-worker |
| duplication by sharding line ranges across workers. |
|
|
