Ceptron

Open foundation models, one family per folder under models/, served by the ceptron Python package (one sub-package per family).

family	answers
temporal_causal	which variables of a time series cause which, at which lag, in one forward pass (built on TCPFN)

Use

Install into a virtual environment (Python 3.10 or newer). Ubuntu, Debian and Homebrew Python refuse a plain pip install outside one:

python3 -m venv ceptron-env
source ceptron-env/bin/activate        # Windows: ceptron-env\Scripts\activate
pip install "git+https://github.com/ProfitOpsAI/ceptron" pandas

On Ubuntu or Debian, if python3 -m venv says ensurepip is not available, run sudo apt install python3-venv first. Or with uv, which needs no system packages:

uv venv ceptron-env
source ceptron-env/bin/activate
uv pip install "git+https://github.com/ProfitOpsAI/ceptron" pandas

Then run python in that environment and paste this, or save it as quickstart.py and run python quickstart.py. It builds a small series with two planted causes and asks the model to find them; the first run downloads the model (about 260 MB):

import numpy as np
import pandas as pd
from ceptron.temporal_causal import CausalModel

# A toy series with two planted causes: price drives demand 2 steps later, ads drive visits 3 steps later.
rng = np.random.default_rng(0)
T = 400
x = np.zeros((T, 4))
noise = rng.normal(size=(T, 4))
for t in range(3, T):
    x[t] = 0.4 * x[t - 1] + noise[t]
    x[t, 1] += 0.9 * x[t - 2, 0]   # price -> demand, lag 2
    x[t, 3] += 0.8 * x[t - 3, 2]   # ads -> visits, lag 3
df = pd.DataFrame(x, columns=["price", "demand", "ads", "visits"])

model = CausalModel.from_pretrained("ProfitOpsAI/ceptron", model="XS-discovery")
result = model.discover(df)
print(result)

You should see (the last digit can differ on other hardware):

Discovery: 3 edges over 4 variables (graph head logit +22.53)
  ads -> visits  lag 3  p 0.79
  price -> demand  lag 2  p 0.76
  demand -> price  lag 23  p 0.28

Both planted causes come first, with the right lags. The third, much weaker link is a false alarm: XS marks as many links as its probabilities add up to, and on very small graphs that count tends to be one too many, so read the probabilities, not only the list.

On your own data, one column per variable and one row per time step (drop the timestamp column):

df = pd.read_csv("my_series.csv").drop(columns=["timestamp"])
result = model.discover(df, col_types={"orders": "count"})   # declare count, binary, ordinal, bounded columns
print(result)
result.to_frame()                                             # the marked links as a DataFrame

For an irregular clock, pass time_gap: the gap before each row, in steps of the regular clock.

Models

Each model folder holds its own card, config.json and model.safetensors.

family	model	parameters	weights sha256
temporal_causal	XS-discovery	65.5 M	3366c5844caa4fb1...

Sizes

Each family comes in sizes. XS is available now; S, M and L are coming soon. XS trades accuracy for speed: it is the fast first look. For higher precision, recall and F1, harder series and more of the causal questions the models answer natively, use S, M and L, coming soon; each release publishes its own measured numbers, so you can pick the size by the accuracy you need.

size	status	good for
XS	available: XS-discovery	a fast first causal map of a time series in seconds on a CPU or a fraction of a second on a GPU (100 columns × 400 rows: 0.8 s CPU, 0.03 s GPU): which variables drive which, and after how many steps. Strongest on process/operations and retail/demand series (AUROC 94 % and 88 % on our development set). What XS is good for
S	coming soon	higher precision, recall and F1 than XS; harder series
M	coming soon	higher precision, recall and F1 than XS; harder series
L	coming soon	higher precision, recall and F1 than XS; harder series

License

The model weights, configurations and cards in this repository are licensed under CC BY-NC 4.0 (see LICENSE): anyone may use, study, modify and share them for non-commercial purposes, including research, teaching and personal projects. Commercial use of the models needs a separate license from ProfitOps Inc. The ceptron code on GitHub is Apache 2.0.