dataset_guide/README.md

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:

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.