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README.md

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 license: other
 license_name: ci-proto-licence
 license_link: https://huggingface.co/ContinuousIntelligence/ci-proto/blob/main/LICENCE
+library_name: continuousintelligence
+pipeline_tag: time-series-forecasting
+tags:
+- time-series
+- forecasting
+- probabilistic-forecasting
+- foundation-model
 ---
+
+<img src="assets/readme/header.svg" alt="ci-proto · Continuous Intelligence" width="1200">
+
+# Introducing ci-proto, our frontier model that says goodbye to context windows and hello to accuracy
+
+**State-of-the-art forecasting at your fingertips.**
+
+- **2M+ context window.** Process 64× the history of any other time-series foundation
+  model.
+- **Frontier accuracy.** Leading forecasting performance on fev-bench.
+- **CPU and GPU support.** Run locally through the same Python API.
+- **Multivariate forecasting.** Turn relationships between signals into more accurate
+  forecasts.
+- **Uncertainty included.** Get nine quantiles between 0.1 and 0.9 to plan for a range of
+  outcomes.
+
+**[Make your first forecast](#make-your-first-forecast) ·
+[See the results](#frontier-performance) ·
+[Join the waitlist for our next release](https://www.continuousintelligence.co.uk/waitlist) ·
+[Join our Design Partner Program](https://docs.google.com/forms/d/e/1FAIpQLSdn59prmnz5KpJtVQlyo1FFGLA_YgIB93Z5E5pVIvpTYBnFuw/viewform)**
+
+## Make your first forecast
+
+**Your first forecast in a few lines.** Install with Python 3.10–3.14:
+
+```sh
+pip install continuousintelligence
+```
+
+Forecast the next day of bicycle rentals from five observed days of hourly demand
+in Washington, D.C. The example history is included with the package:
+
+```python
+import continuousintelligence as ci
+
+# Load the model (uses CUDA when available).
+model = ci.load_model("ContinuousIntelligence/ci-proto")
+
+# Load example history as a torch.Tensor shaped [variables, time].
+history = ci.load_example()
+
+# Forecast the next 24 hourly time steps.
+prediction = model.forecast(history, horizon=24)
+
+# Plot the forecast and its uncertainty bands.
+ci.plot_forecast(history, prediction, model.quantiles, output="forecast.png")
+```
+
+![Five days of hourly bicycle rentals followed by ci-proto's next-day median
+forecast and nested quantile bands.](assets/readme/bike_sharing_forecast.png)
+
+**The next 24 hours, with uncertainty included.** The shaded region marks the forecast
+day. The curve shows the model's median forecast, surrounded by nested quantile bands.
+Replace the example history with your own to get started.
+
+Data: Hadi Fanaee-T (2013), [Bike Sharing, UCI](https://doi.org/10.24432/C5W894),
+licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+
+**Dive deeper.** The [walkthrough notebook][walkthrough] extends this example with calendar
+covariates, practical uses of the quantiles, and forecasting through missing data.
+
+[walkthrough]: https://github.com/continuousintelligence/ci/blob/main/examples/walkthrough.ipynb
+
+## Frontier performance
+
+**The #1 foundation model on fev-bench.**
+
+![Mean scaled quantile loss on fev-bench versus parameter count for ci-proto and other
+forecasting models. Lower is better.](assets/readme/ci_proto_pareto_frontier.png)
+
+## Talk to the team
+
+**Tell us what you need to predict.** We want to hear about the decisions you're trying to
+improve, the data you're working with, and where current methods fall short.
+
+Whether you're exploring a new idea or improving an existing system, we'd like to work
+with you on a solution. Share your use case, your constraints, and what success would look
+like, and let's discuss how we can help.
+
+**[Join our Design Partner Program](https://docs.google.com/forms/d/e/1FAIpQLSdn59prmnz5KpJtVQlyo1FFGLA_YgIB93Z5E5pVIvpTYBnFuw/viewform)**
+
+## Beyond ci-proto
+
+**Frontier performance is our starting point.** ci-proto gives you a first look at our
+approach to time-series intelligence. Our next release builds on that foundation.
+Join the waitlist to be the first to hear what's new, see the results, and find out when
+you can try it.
+
+**[Join the waitlist](https://www.continuousintelligence.co.uk/waitlist)**
+
+## Usage details
+
+Covariates, categorical inputs, timestamps, related series, revision pinning, and CPU and
+GPU setup are documented in the [package README](https://github.com/continuousintelligence/ci#usage-details).
+
+## Model details
+
+- **Developed by:** Continuous Intelligence Limited
+- **Model type:** pretrained time-series foundation model for zero-shot probabilistic forecasting
+- **Parameters:** 167M
+- **Inputs:** one or more target series, with optional past and future covariates, timestamps and related series
+- **Outputs:** nine quantiles from 0.1 to 0.9 for every target and forecast step
+- **Files:** `model.ckpt` (histories up to 16,384 steps), `model-long.ckpt` (longer histories, selected
+  automatically), `tta.json` (preprocessing and calibration settings) and `config.json` (model information)
+
+## Training and evaluation
+
+- **Training data:** a large corpus of real-world and synthetic time series.
+- **Evaluation:** zero-shot on all 100 tasks of [fev-bench](https://huggingface.co/spaces/autogluon/fev-leaderboard),
+  with the released preprocessing and calibration in `tta.json`, scored by scaled quantile loss.
+
+## Licence
+
+The model weights are released under the [ci-proto Code and Model Licence](LICENCE).
+
+## Citation
+
+```bibtex
+@misc{ci-proto-2026,
+  title  = {ci-proto},
+  author = {{Continuous Intelligence Limited}},
+  year   = {2026},
+  url    = {https://huggingface.co/ContinuousIntelligence/ci-proto}
+}
+```