---
tags:
- time-series-forecasting
- foundation-models
- pretrained-models
- time-series
- timeseries
- forecasting
- observability
- safetensors
- pytorch_model_hub_mixin
license: apache-2.0
pipeline_tag: time-series-forecasting
thumbnail: https://web-assets.dd-static.net/42588/1778691695-toto-2-hero.png
model-index:
- name: Toto-2.0-22m
results:
- task:
type: time-series-forecasting
dataset:
name: BOOM
type: BOOM
metrics:
- name: CRPS
type: CRPS
value: 0.363
- name: MASE
type: MASE
value: 0.601
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: 0.496
- name: MASE
type: MASE
value: 0.719
source:
name: GIFT-Eval Time Series Forecasting Leaderboard
url: https://huggingface.co/spaces/Salesforce/GIFT-Eval
- task:
type: time-series-forecasting
dataset:
name: TIME
type: TIME
metrics:
- name: CRPS
type: CRPS
value: 0.556
- name: MASE
type: MASE
value: 0.668
source:
name: TIME Benchmark Leaderboard
url: https://huggingface.co/spaces/Real-TSF/TIME-leaderboard
---
# Toto-2.0-22m
Toto (Time Series Optimized Transformer for [Observability](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, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family.
The family sets a new state of the art on three forecasting benchmarks: [BOOM](https://huggingface.co/spaces/Datadog/BOOM), our observability benchmark; [GIFT-Eval](https://huggingface.co/spaces/Salesforce/GIFT-Eval), the standard general-purpose benchmark; and the recent contamination-resistant [TIME](https://arxiv.org/abs/2602.12147) benchmark.
## 📊 Performance
Every Toto 2.0 size sits on or near the Pareto frontier on both BOOM and GIFT-Eval. The three largest sizes rank first, second, and third among foundation models on GIFT-Eval CRPS rank. On TIME, Toto 2.0 sizes take the top three spots on every metric, ahead of every other external foundation model evaluated.
## ⚡ 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-2.0-22m")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device).eval()
# (batch, n_variates, time_steps)
target = torch.randn(1, 1, 512, device=device)
target_mask = torch.ones_like(target, dtype=torch.bool)
series_ids = torch.zeros(1, 1, dtype=torch.long, device=device)
# 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,
decode_block_size=768,
has_missing_values=False,
)
```
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
All five Toto 2.0 sizes share the same training recipe; pick a size based on your accuracy/latency budget. Latency is forward-pass time for a 1,024-step single-pass forecast at batch size 8 on a single A100.
| Model | Params | Weights (fp32) | Latency | Recommended for |
|:---:|:---:|:---:|---|---|
| [Toto‑2.0‑4m](https://huggingface.co/Datadog/Toto-2.0-4m) | 4m | 16 MB | ~3.8 ms | Edge / CPU deployment; tightest latency or memory budgets. |
| [Toto‑2.0‑22m](https://huggingface.co/Datadog/Toto-2.0-22m) | 22m | 84 MB | ~5.0 ms | Efficient default — matches or beats Toto 1.0 quality with ~7× fewer parameters. |
| [Toto‑2.0‑313m](https://huggingface.co/Datadog/Toto-2.0-313m) | 313m | 1.2 GB | ~15.4 ms | Strong general-purpose checkpoint; top-3 foundation model on GIFT-Eval. |
| [Toto‑2.0‑1B](https://huggingface.co/Datadog/Toto-2.0-1B) | 1B | 3.9 GB | ~20.9 ms | Best quality / cost tradeoff for production workloads. |
| [Toto‑2.0‑2.5B](https://huggingface.co/Datadog/Toto-2.0-2.5B) | 2.5B | 9.1 GB | ~36.2 ms | Highest accuracy; #1 foundation model on every benchmark. |
## ✨ 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:** A single training recipe transfers cleanly across all five sizes (4m → 2.5B).
## 🏗️ Architecture
A decoder-only patched transformer whose attention layers alternate between time-axis (causal) and variate-axis (full) views of the input. Toto 2.0 adds contiguous patch masking (CPM) for single-pass parallel decoding, a quantile output head trained with pinball loss, a robust arcsinh input scaler, residual MLP patch projections, and is trained with NorMuon. See the technical report for details.
## 🔗 Additional Resources
- **Technical Report** — *(coming soon)*
- [Blog Post](https://www.datadoghq.com/blog/ai/toto-2/)
- [GitHub Repository](https://github.com/DataDog/toto)
- [Toto 2.0 Collection](https://huggingface.co/collections/Datadog/toto-20) — all five base checkpoints
- [BOOM Dataset](https://huggingface.co/datasets/Datadog/BOOM) — Datadog's observability time-series benchmark
- [Toto 1.0 Weights](https://huggingface.co/Datadog/Toto-Open-Base-1.0)
## 📖 Citation
```bibtex
(citation coming soon)
```
