Base ModelCausal (discovery & effects)
Ceptron XS-discovery · Temporal Causal Discovery
Which variable drives which, at which lag · one forward pass
ProfitOps · CeptronCC BY-NC 4.0 (weights; commercial use needs a ProfitOps license) · ceptron code Apache-2.0
Use for a fast first causal map of your operations or sales data — which numbers drive which others, and how many days later — before you invest in a deeper analysis. Inference-only. Read the probabilities, not only the list: on very small graphs it tends to mark one link too many.
AUROC
94%
process / operations · development set
AUROC
88%
retail / demand · development set
Params
65.5M
built on TCPFN
Run it
CLI
git clone https://github.com/profitopsai/pfnstudio && cd pfnstudio
pip install -e packages/core -e packages/cli
pfnstudio run ceptron-xs-discoveryPulls the pretrained weights from Hugging Face and runs the model's own held-out sample, printing the tasks it supports, a lock digest pinning exactly what was pulled, and a live result. Pass --input your.json to run your data.
Or in the studio: install this base model → then Predict.