Time Series Forecasting
Transformers
lightgbm_multihorizon
feature-extraction
cgm
time-series
glucose-forecasting
lightgbm
metabonet
custom_code
Instructions to use anonymous-4FAD/LightGBM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anonymous-4FAD/LightGBM with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("anonymous-4FAD/LightGBM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # Upload hub/lightgbm to the Hugging Face Hub. | |
| # Run from anywhere; the script resolves its own location. | |
| # | |
| # Override the destination repo via the REPO env var (default: | |
| # anonymous-4FAD/LightGBM). Extra args are forwarded to ``huggingface-cli upload``. | |
| # | |
| # Requires: | |
| # - huggingface-cli installed (it ships with huggingface_hub). | |
| # - You are logged in: ``huggingface-cli login``. | |
| set -euo pipefail | |
| HERE="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| REPO="${REPO:-anonymous-4FAD/LightGBM}" | |
| echo "Uploading ${HERE} -> ${REPO}" | |
| huggingface-cli upload "$REPO" "$HERE" . --repo-type model "$@" | |