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
File size: 620 Bytes
53e13eb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | #!/usr/bin/env bash
# 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" "$HERE" . --repo-type model "$@"
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