Distillation assets

Processed training-data tensors, model weights and an EMA checkpoint. Only a selected subset of the dataset is included. The full metadata ordering is retained; files absent from this subset must be obtained separately for other training schedules.

Download and restore

Downloads require approved access. Request access on the dataset page and sign in with an approved account before downloading.

python3 -m pip install -U huggingface_hub zstandard
hf auth login
hf download mingyang-wu/Distillation --repo-type dataset \
  --include 'assets-v1/*' --local-dir ./asset-download
python3 ./asset-download/assets-v1/restore.py --dest ./project

project/
├── data/
├── models/base-transformer/
├── checkpoints/step-2100/ema/
└── metadata/training-manifest.json

Archive streams are split into 2 GiB parts. Keep all parts together. The restore script verifies archive and individual-file SHA256 checksums. Existing files at matching destination paths are replaced. Metadata file paths are relative to the restored project directory.

Tensor payloads are preserved. Numerical model settings are included without automatic class-dispatch or source-location metadata. Model implementations, training code, public model components and runtime dependencies are supplied separately; this asset repository does not install or launch training.