Parameter Golf Submission

Use this skill when creating or reviewing a Parameter Golf submission folder, independent of the cloud provider used for the run.

Record Folder Contract

A submission folder must contain:

text
records/<track>/<submission-name>/
  README.md
  submission.json
  train_gpt.py
  train.log          # after a real run

train_gpt.py must compile and run from inside this folder in a clean Parameter Golf checkout.

Competition Constraints To Preserve

• Artifact cap: 16,000,000 decimal bytes.
• Training cap for leaderboard records: 10 minutes on 8xH100 SXM-class hardware.
• Evaluation metric: FineWeb validation bits per byte (val_bpb).
• No validation-set leakage. Test-time training may only use validation tokens already scored, if implemented.
• No hidden downloads/network calls during evaluation.
• No local repository imports unless included and counted in the record folder.
• If tokenizer changes, prove BPB accounting carefully; stock SP1024 is safest for first participation.

Self-Contained Script Checklist

Before running:

• ] python -m py_compile train_gpt.py passes.
• ] Imports are standard/allowed environment packages only (torch, numpy, sentencepiece, etc.).
• ] DATA_PATH and TOKENIZER_PATH are env-configurable.
• ] Script loads fineweb_train_*.bin and fineweb_val_*.bin with the Parameter Golf binary header format.
• ] Script computes validation BPB from SentencePiece byte accounting, not just token loss.
• ] Script logs parameter count and artifact-size estimate.
• ] Script writes a compressed artifact, usually final_model.int8.ptz.
• ] Script reloads/dequantizes the compressed artifact and evaluates the round-trip model.
• ] Final log includes final_int8_zlib_roundtrip_exact.

README Contents

The README must include:

• short architecture summary
• dataset/tokenizer used
• exact command
• run hardware and time budget
• final metrics after run
• artifact-size line after run
• caveats if the run is smoke/non-record/pending verification

submission.json Contents

Use actual values after the run, not placeholders:

json
{
  "run_name": "...",
  "author": "...",
  "github_id": "...",
  "track": "track_10min_16mb or track_non_record_16mb",
  "val_bpb": 1.2345,
  "val_loss": 2.1234,
  "artifact_size_bytes": 12345678,
  "command": "...",
  "status": "completed"
}

Add architecture fields as useful, but avoid claiming record eligibility unless the log proves it.

Post-Run Extraction

After a run, extract these lines:

bash
grep -E "final_int8_zlib_roundtrip_exact|Total submission size int8\+zlib|stopping_early|train_time|model_params" train.log

Update:

• submission.json.val_bpb
• submission.json.val_loss
• submission.json.artifact_size_bytes
• README metrics section

Status Labels

Use precise status:

• prepared_pending_run: folder created, no real run yet
• smoke_passed: short/non-final run passed
• completed_non_record: full run but not leaderboard-valid or not SOTA
• completed_record_candidate: 8xH100 10-minute compliant run with full log and artifact under cap
• failed: include failure reason and last good checkpoint/log line

Common Failure Modes

• Accidentally importing local model code (from src...) not present in record folder.
• Forgetting to copy train.log from logs/<RUN_ID>.txt.
• Reporting pre-quant BPB instead of int8 round-trip BPB.
• Exceeding 16MB after counting code + compressed artifact.
• Running on 1 GPU and calling it leaderboard-valid.
• Using a custom tokenizer without exact byte accounting proof.