abel-lab/nanoTSFM-pretrain
nanoTSFM-pretrain Hill-climbing GIFT-Eval with one A100 and one hour. Pretraining data for nanoTSFM, an open challenge where small time-series foundation models train on one GPU and forecast data they have never seen. The baseline trains on GEP-M in two minutes on an A100 and scores 0.670 relative CRPS on GIFT-Eval, zero-shot. The record page tracks the best result so far. from datasets import load_dataset train = load_dataset("abel-lab/nanoTSFM-pretrain", "GEP-M"… See the full description on the dataset page: https://huggingface.co/datasets/abel-lab/nanoTSFM-pretrain.
| Author / organization | abel-lab |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 418 (as reported by Hugging Face) |
| Likes | 1 |
| Created | 28 September 2026 |
| Last updated at source | 30 September 2026 |
Popularity, last 3 days
418 now (+11 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.