Moirai-1.0-R-Small

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Salesforce/moirai-1.0-R-small

14M params | 512 context | $0.00025 per forecast

Moirai-1.0-R-Small is the smallest dense checkpoint in Salesforce's original Moirai family. It uses a masked-encoder forecasting architecture with any-variate attention and a probabilistic mixture output head, letting it reason over arbitrary numbers of target variables and dynamic covariates. It is the most cost-efficient way to access the first-generation Moirai design.

Model Classification

Family

Moirai

Type

time series foundation model

Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.

Resources

HuggingFace Paper

Training Data

LOTSA, the Large-scale Open Time Series Archive, with roughly 27B observations across nine domains including energy, transport, finance, healthcare, sales, climate, web, and social data.

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

forecastingquantile-forecastingmultivariatecovariateszero-shot

Specifications

Parameters: 14M
Architecture: masked encoder transformer with multi-patch projections, any-variate attention, and mixture-distribution output
Context length: 512
Max context: 8,192
Minimum history: n/a
Recommended history: n/a
Input step: n/a
Required target series: 1
Temperature: Ignored
Top P: Ignored
Max output: 1,024
Avg latency: n/a
Uptime: n/a
Plan limits: 1,000 rpm free · 1,000,000 rpm with billing
Accelerator: T4
Regions: Virginia, US
License: n/a

Pricing

Per forecast: $0.00025

Performance

Average latency: n/a
Availability: n/a
Plan limits: 1,000 rpm free · 1,000,000 rpm with billing