Datasets:
File size: 1,757 Bytes
6eb5c20 5eb8e0b 4382ed7 5eb8e0b 6eb5c20 5eb8e0b 6eb5c20 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # 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.
|