Hugging Face Artifact Export

The project can publish a small dataset and model checkpoint without redistributing proprietary or public-provider market data. Both artifacts come from the deterministic synthetic quick start.

Artifact repositories

Hugging Face repository	Contents	Intended use
dddyym/ml-quant-trading-synthetic	Live viewer-ready 100,000-row compressed CSV, generator config, checksum manifest, dataset card	Installation, CI, teaching, and pipeline smoke tests
dddyym/ml-quant-trading-synthetic-mlp	Live PyTorch checkpoint, 213-factor order, architecture config, metrics, model card	Checkpoint loading and inference smoke tests

Both public repositories were verified with anonymous downloads; the dataset viewer renders its schema and rows, and the checkpoint passes the documented loading and inference smoke test. Neither artifact represents real instruments, proprietary data, deployable alpha, or evidence of live performance.

Build locally

Run the deterministic pipeline, then export the two repository directories:

mlquant demo
python scripts/export_huggingface_artifacts.py \
  --artifacts-dir artifacts/small \
  --config configs/small.yaml \
  --output-dir artifacts/huggingface \
  --namespace dddyym

The exporter writes:

artifacts/huggingface/
├── bundle_manifest.json
├── ml-quant-trading-synthetic/
│   ├── README.md
│   ├── artifact_manifest.json
│   ├── source_config.yaml
│   └── data/synthetic_ohlcv.csv.gz
└── ml-quant-trading-synthetic-mlp/
    ├── README.md
    ├── artifact_manifest.json
    ├── config.json
    ├── feature_names.json
    ├── metrics.json
    ├── pytorch_model.bin
    └── source_config.yaml

The dataset gzip stream uses a fixed timestamp so the same panel produces the same SHA-256 digest. Manifests record the source commit and file checksums.

Maintainer upload after authentication

Install and authenticate the Hugging Face CLI locally. Never paste a full token into an issue, PR, notebook, or chat transcript.

python -m pip install --upgrade huggingface_hub
hf auth login

Create and upload the dataset repository:

hf repo create dddyym/ml-quant-trading-synthetic --repo-type dataset --exist-ok
hf upload dddyym/ml-quant-trading-synthetic \
  artifacts/huggingface/ml-quant-trading-synthetic . \
  --repo-type dataset

Create and upload the model repository:

hf repo create dddyym/ml-quant-trading-synthetic-mlp --exist-ok
hf upload dddyym/ml-quant-trading-synthetic-mlp \
  artifacts/huggingface/ml-quant-trading-synthetic-mlp .

After a new upload:

1. Confirm the dataset viewer renders rows and column types.
2. Run the model-card loading snippet in a clean environment.
3. Link both repositories from the Hugging Face paper page.
4. Update Issue #1 with the live URLs and close it only after both smoke checks pass.

Safety boundary

The exporter refuses configs without a synthetic section. It accepts only the local demo artifacts supplied on the command line; it does not contain any market-data downloader or Hub credential handling.