Datasets:
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:
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.mdincheckpoint_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.