Super-squash branch 'main' using huggingface_hub

Co-authored-by: LTMeyer <LTMeyer@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,38 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/enedis_with_holidays.png filter=lfs diff=lfs merge=lfs -text
+assets/medicam_with_cov.png filter=lfs diff=lfs merge=lfs -text
+assets/medicam_without_cov.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,189 @@
+---
+license: apache-2.0
+pipeline_tag: time-series-forecasting
+library_name: pytorch
+thumbnail: https://www.theforecastingcompany.com/images/og.png
+tags:
+  - time-series
+  - forecasting
+  - foundation-models
+  - pretrained-models
+  - safetensors
+model-index:
+  - name: t0
+    results:
+      - task:
+          type: time-series-forecasting
+          name: Time Series Forecasting
+        dataset:
+          name: fev-bench
+          type: autogluon/fev-bench
+        metrics:
+          - name: Skill score
+            type: skill-score
+            value: 42.5
+        source:
+          name: fev-bench leaderboard
+          url: https://huggingface.co/spaces/autogluon/fev-bench
+      - task:
+          type: time-series-forecasting
+          name: Time Series Forecasting
+        dataset:
+          name: GIFT-Eval
+          type: Salesforce/GiftEval
+        metrics:
+          - name: CRPS
+            type: crps
+            value: 0.5126
+          - name: MASE
+            type: mase
+            value: 0.7431
+        source:
+          name: GIFT-Eval leaderboard
+          url: https://huggingface.co/spaces/Salesforce/GIFT-Eval
+---
+
+<p align="center">
+  <img class="dark:hidden" src="https://www.theforecastingcompany.com/logo/logo_horizontal_pride_light.png" alt="The Forecasting Company" width="280" />
+  <img class="hidden dark:block" src="https://www.theforecastingcompany.com/logo/logo_horizontal_pride_dark.png" alt="The Forecasting Company" width="280" />
+</p>
+
+# `t0`
+
+Open-weights time-series forecasting foundation model from [The Forecasting Company](https://theforecastingcompany.com/).
+`t0` is a transformer-based model that
+produces probabilistic multi-horizon forecasts and natively operates on
+multiple covariates. `t0-alpha` is our first iteration of the model.
+
+You can use `t0` on [Retrocast](https://app.retrocast.com/), our platform for forecasting on your own data. You can also compare forecast across different open-weight models.
+
+![t0 forecasting French national electricity demand in Retrocast](assets/enedis_with_holidays.png)
+
+_`t0` forecasting French national electricity demand in Retrocast. Data:
+[Enedis open data](https://data.enedis.fr/)._
+
+## 📈 Forecasting with covariates
+
+`t0` leverages covariate information, in the past and future when
+available, to improve its forecast.
+
+| Without covariates                                                | With covariates                                             |
+| ----------------------------------------------------------------- | ----------------------------------------------------------- |
+| ![t0 forecast without covariates](assets/medicam_without_cov.png) | ![t0 forecast with covariates](assets/medicam_with_cov.png) |
+
+_Data: [Medic'AM](https://www.assurance-maladie.ameli.fr/etudes-et-donnees/medicaments-classe-atc-medicam),
+monthly drug reimbursements from the French national health insurance._
+
+The [Quickstart](#-quickstart) below shows the API for both a plain
+univariate forecast and a multivariate forecast that conditions on
+historical and known-future covariates.
+
+## 🚀 Quickstart
+
+```bash
+pip install tfc-t0
+```
+
+The simplest path is a univariate forecast through `predict`:
+
+```python
+import torch
+from t0 import T0Forecaster
+
+model = T0Forecaster.from_pretrained("theforecastingcompany/t0-alpha").eval()
+
+context = torch.randn(4, 512)  # 4 series, 512 past timesteps
+out = model.predict(context, horizon=64, quantiles=[0.1, 0.5, 0.9])
+out.quantiles  # (4, 64, 3)
+out.median     # (4, 64)
+```
+
+`predict` accepts `numpy` arrays. 1-D contexts are auto-promoted to a
+single-row batch. NaN values in the context are treated as missing
+observations.
+
+### Forecasting with covariates
+
+Anything you know over the **past** goes in `context` — alongside the
+target, extra variates attend to it and are forecast together. Anything
+you know over the **future** (calendar features, planned promotions,
+weather forecasts) goes in `future_covariates`, shaped
+`[B, F, context + horizon]`; the model conditions on it but does not
+forecast it.
+
+```python
+import torch
+from t0 import T0Forecaster
+
+model = T0Forecaster.from_pretrained("theforecastingcompany/t0-alpha").eval()
+
+context = torch.randn(2, 512)                    # 2 series, 512 past timesteps
+future_covariates = torch.randn(2, 3, 512 + 64)  # 3 covariates known over context + horizon
+
+out = model.predict(
+    context,
+    horizon=64,
+    quantiles=[0.1, 0.5, 0.9],
+    future_covariates=future_covariates,
+)
+out.quantiles  # (2, 64, 3)
+out.median     # (2, 64)
+```
+
+## 🏗️ Architecture
+
+`t0` is a decoder-style patch transformer that alternates time and
+covariate attention layers. It predicts 5 quantiles (0.1, 0.25, 0.5,
+0.75, 0.9), decoding multiple horizons in parallel — up to 1024
+timesteps in one forward pass — and falling back on autoregressive
+rollout for longer horizons.
+
+|                 |                           |
+| --------------- | ------------------------- |
+| Parameters      | ~102M                     |
+| Layers          | 24                        |
+| Embedding dim   | 512                       |
+| Feedforward dim | 2048                      |
+| Attention heads | 8                         |
+| Patch size      | 32                        |
+| Quantile levels | 0.1, 0.25, 0.5, 0.75, 0.9 |
+
+### 🧬 Lineage
+
+`t0` builds on ideas — and in places, code — from open-source forecasting
+models. We gratefully acknowledge:
+
+- **Toto** by Datadog ([repo](https://github.com/DataDog/toto)) &
+  **Chronos-2** by Amazon
+  ([repo](https://github.com/amazon-science/chronos-forecasting)) —
+  factorizing attention in the time and variates dimension.
+- **TiRex** by NXAI
+  ([repo](https://github.com/NX-AI/tirex)) — contiguous patch masking.
+
+Code-level attributions are listed in [`NOTICE`](NOTICE), all under
+Apache-2.0.
+
+## 🧰 Public API
+
+- `T0Forecaster` — `nn.Module` with `from_pretrained` /
+  `save_pretrained` (via `huggingface_hub.PyTorchModelHubMixin`) and the
+  user-facing `predict(context, horizon, quantiles, future_covariates)`.
+- `T0Config` — frozen dataclass; `T0Config.medium()` is the published
+  configuration.
+
+## 📚 Citation
+
+```bibtex
+@misc{tfc-t0,
+  title  = {t0: A time-series forecasting foundation model},
+  author = {The Forecasting Company},
+  year   = {2026},
+  url    = {https://huggingface.co/theforecastingcompany/t0-alpha},
+}
+```
+
+## ⚖️ License
+
+Apache-2.0 — see [LICENSE](LICENSE) and [NOTICE](NOTICE).
+</content>
+</invoke>

config.json

@@ -0,0 +1,17 @@
+{
+  "embed_dim": 512,
+  "num_layers": 24,
+  "num_heads": 8,
+  "mlp_hidden_dim": 2048,
+  "patch_size": 32,
+  "group_every_n": 3,
+  "dropout": 0.1,
+  "quantile_levels": [
+    0.1,
+    0.25,
+    0.5,
+    0.75,
+    0.9
+  ],
+  "scaler_use_arcsinh": true
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f795594503b90a9fa1565c9e3c0c3523d16b971f283fc4c815a88b1ae2459aa7
+size 406601492