Moirai-2.0-R-Small
Model directorySalesforce/moirai-2.0-R-small~11M params | Upstream checkpoint
Moirai-2.0-R-Small is the faster successor to the original dense Moirai family, the small checkpoint in the newer 2.0-R line. At roughly 11M parameters it is a deliberately compact model: Salesforce reports performance that surpasses larger earlier Moirai checkpoints, so the 2.0 redesign is meant to deliver more accuracy per parameter than the first-generation 1.0-R and 1.1-R families.
It marks an architectural break from the masked-encoder Moirai design, switching to a decoder-only transformer with quantile loss and multi-token prediction, plus better missing-value handling. The quantile loss yields calibrated probabilistic multivariate forecasts directly, while multi-token prediction speeds up decoding. Pretraining draws on a subset of GIFT-Eval Pretrain and Train, Chronos mixup data, KernelSynth synthetic series, and internal Salesforce operational data, as listed in the official model card.
Where to run this model
Compute availability, hardware requirements, and pricing depend on the execution provider.
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- Birla AI Labs Forecasting API
provider listing · Source checked 2026-10-06
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- Retrocast · The Forecasting Company
managed service · Source checked 2026-10-06
Forecast through the managed API using moirai-2. The provider controls the deployed variant and request limits.
- Community forecasting playground
hosted demo · Source checked 2026-10-06
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Model Classification
Family
Moirai-2.0-R
Type
time series foundation model
Pretrained time-series model for zero-shot or few-shot forecasting workloads.
Resources
Training Data
Subset of GIFT-Eval Pretrain and Train, Chronos mixup data, KernelSynth synthetic series, and internal Salesforce operational data, as listed in the official model card.
Recommended For
- • Multivariate forecasting across heterogeneous domains
- • Workloads that benefit from probabilistic outputs and arbitrary variate counts
Strengths
- • Strong multivariate coverage across the Moirai family
- • Well-suited to covariates and correlated series
Limitations
- • Model cards for some newer Moirai variants are still sparse on exact checkpoint details
- • Heavier family choices can be more expensive than tiny single-purpose baselines
Capabilities
Tags
Specifications
- Parameters
- ~11M
- Architecture
- decoder-only transformer with quantile loss and multi-token prediction
- Context length
- n/a
- Max context
- n/a
- Minimum history
- n/a
- Recommended history
- n/a
- Input step
- n/a
- Required target series
- 1
- Temperature
- Ignored
- Top P
- Ignored
- Max output
- n/a
- License
- n/a