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gsoykan/
exoprompt-checkpoints

Name: exoprompt-checkpoints
Availability: InStock

pytorch

time-series-forecasting

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ExoPrompt — Greenhouse Climate Checkpoints

PyTorch Lightning checkpoints for the ExoPrompt paper:

ExoPrompt: Transformer-based greenhouse climate forecasting with structured conditioning and physics-based simulation. Gürkan Soykan, Önder Babur, Qingzhi Liu, Bedir Tekinerdogan. Computers and Electronics in Agriculture, vol. 246, p. 111673, 2026. DOI: 10.1016/j.compag.2026.111673

These are the pretraining checkpoints used to produce the paper's quantitative results on the 200k-sample GreenLight subsets. Each checkpoint corresponds to the best validation epoch of a Transformer backbone trained on one lighting scenario, in either the vanilla or ExoPrompt-conditioned configuration.

Files

Filename	Variant	Lighting scenario	Architecture
`200k_exo_hps.ckpt`	ExoPrompt	HPS	Transformer + exo-prompt projector (254-d)
`200k_exo_led.ckpt`	ExoPrompt	LED	Transformer + exo-prompt projector (254-d)
`200k_exo_mixed.ckpt`	ExoPrompt	Mixed (HPS + LED)	Transformer + exo-prompt projector (254-d)
`200k_vanilla_hps.ckpt`	Vanilla	HPS	Transformer (no exogenous conditioning)
`200k_vanilla_led.ckpt`	Vanilla	LED	Transformer (no exogenous conditioning)
`200k_vanilla_mixed.ckpt`	Vanilla	Mixed (HPS + LED)	Transformer (no exogenous conditioning)

All six share the same Transformer backbone hyperparameters and the same forecasting head (pred_len = 96, label_len = 48, 3-feature indoor-climate output: tAir, vpAir, co2Air).

Usage

These checkpoints are designed to load with the exoprompt-inference Streamlit demo, or directly via the official ExoPrompt repo. Minimal example:

python

from huggingface_hub import hf_hub_download
from exoprompt_inference.inference.model_loader import (
    load_model_from_checkpoint,
    ModelType,
)

ckpt_path = hf_hub_download(
    repo_id="gsoykan/exoprompt-checkpoints",
    filename="200k_exo_hps.ckpt",
)
model = load_model_from_checkpoint(
    ckpt_path,
    model_type=ModelType.TIME_SERIES_LIB_MODEL,
    device="cpu",
)

License

MIT — same as the source code in the official ExoPrompt repo.

Citation

bibtex

@article{SOYKAN2026111673,
  title   = {ExoPrompt: Transformer-based greenhouse climate forecasting with structured conditioning and physics-based simulation},
  journal = {Computers and Electronics in Agriculture},
  volume  = {246},
  pages   = {111673},
  year    = {2026},
  author  = {Soykan, G{\"u}rkan and Babur, {\"O}nder and Liu, Qingzhi and Tekinerdogan, Bedir}
}