Push model using huggingface_hub.

README.md

@@ -1,134 +1,10 @@
 ---
 tags:
-- time-series-forecasting
-- foundation-models
-- pretrained-models
-- time-series
-- timeseries
-- forecasting
-- observability
-- safetensors
+- model_hub_mixin
 - pytorch_model_hub_mixin
-license: apache-2.0
-pipeline_tag: time-series-forecasting
-thumbnail: https://corp.dd-static.net/img/about/presskit/kit/press_kit.png
-model-index:
-- name: Toto-2.0-313m
-  results:
-    - task: 
-        type: time-series-forecasting
-      dataset:
-        name: BOOM
-        type: BOOM
-      metrics:
-        - name: CRPS
-          type: CRPS
-          value: 0.351
-        - name: MASE
-          type: MASE
-          value: 0.585
-      source:
-        name: BOOM 💥 Observability Time-Series Forecasting Leaderboard
-        url: https://huggingface.co/spaces/Datadog/BOOM
-    - task: 
-        type: time-series-forecasting
-      dataset: 
-        name: GIFT-Eval
-        type: GIFT-Eval
-      metrics:
-        - name: CRPS
-          type: CRPS
-          value: 0.481
-        - name: MASE
-          type: MASE
-          value: 0.703
-      source:
-        name: GIFT-Eval Time Series Forecasting Leaderboard
-        url: https://huggingface.co/spaces/Salesforce/GIFT-Eval
-  
 ---
-# Toto-2.0-313m
 
-Toto (**T**ime Series **O**ptimized **T**ransformer for [**O**bservability](https://www.datadoghq.com/knowledge-center/observability/)) is a family of time series foundation models for multivariate forecasting developed by [Datadog](https://www.datadoghq.com/). **Toto 2.0** is the current generation, featuring u-μP-scaled transformers ranging from 4M to 2.5B parameters.
-
----
-
-## ✨ Key Features
-
-- **Zero-Shot Forecasting**: Forecast without fine-tuning on your specific time series.
-- **Multi-Variate Support**: Efficiently process multiple variables using alternating time/variate attention.
-- **Probabilistic Predictions**: Generate point forecasts and uncertainty estimates via a quantile output head.
-- **Decoder-Only Architecture**: Support for variable prediction horizons and context lengths.
-- **u-μP Scaling**: Stable training transfer across all model sizes.
-
-<div style="width: 100%; margin: auto; padding: 1rem;">
-  <img src="figures/architecture.png" alt="Toto 2.0 architecture" style="width: 100%; height: auto;" />
-  <em style="display: block; margin-top: 0.5rem; text-align: center;">
-    Overview of the Toto 2.0 architecture.
-  </em>
-</div>
-
----
-
-## ⚡ Quick Start
-
-Inference code is available on [GitHub](https://github.com/DataDog/toto).
-
-### Installation
-
-```bash
-pip install "toto-2 @ git+https://github.com/DataDog/toto.git#subdirectory=toto2"
-```
-
-### Inference Example
-
-```python
-import torch
-from toto2 import Toto2Model
-
-model = Toto2Model.from_pretrained("Datadog/Toto-2.0-313m")
-model = model.to("cuda").eval()
-
-# (batch, n_variates, time_steps)
-target = torch.randn(1, 1, 512, device="cuda")
-target_mask = torch.ones_like(target, dtype=torch.bool)
-series_ids = torch.zeros(1, 1, dtype=torch.long, device="cuda")
-
-# Returns quantiles of shape (9, batch, n_variates, horizon)
-# Quantile levels: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
-quantiles = model.forecast(
-    {"target": target, "target_mask": target_mask, "series_ids": series_ids},
-    horizon=96,
-)
-```
-
-For more examples, see the [Quick Start notebook](https://github.com/DataDog/toto/blob/main/toto2/notebooks/quick_start.ipynb) and [GluonTS integration notebook](https://github.com/DataDog/toto/blob/main/toto2/notebooks/gluonts_integration.ipynb).
-
----
-
-## 💾 Available Checkpoints
-
-| Checkpoint | Parameters |
-|---|---|
-| [Toto-2.0-4m](https://huggingface.co/Datadog/Toto-2.0-4m) | 4M |
-| [Toto-2.0-22m](https://huggingface.co/Datadog/Toto-2.0-22m) | 22M |
-| [Toto-2.0-313m](https://huggingface.co/Datadog/Toto-2.0-313m) | 313M |
-| [Toto-2.0-1B](https://huggingface.co/Datadog/Toto-2.0-1B) | 1B |
-| [Toto-2.0-2.5B](https://huggingface.co/Datadog/Toto-2.0-2.5B) | 2.5B |
-
----
-
-## 🔗 Additional Resources
-
-- **[Blog Post](https://www.datadoghq.com/blog/ai/toto-2/)**
-- **[GitHub Repository](https://github.com/DataDog/toto)**
-- **[BOOM Dataset](https://huggingface.co/datasets/Datadog/BOOM)**
-- **[Toto 1.0 Weights](https://huggingface.co/Datadog/Toto-Open-Base-1.0)**
-
----
-
-## 📖 Citation
-
-```bibtex
-(citation coming soon)
-```
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: [More Information Needed]
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]

model.safetensors

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