Sundial (Base 128M)
onlinethuml/sundial-base-128m128M params | 3K context | $0.00025 per forecast
Sundial is THUML's native generative TSFM for continuous-valued forecasting. The official card presents it as a 128M-parameter decoder-only model trained with TimeFlow Loss, built to generate multiple plausible futures without relying on discrete tokenization. It is one of the most directly probabilistic models in the hosted catalog and a strong option when distribution quality matters as much as point accuracy.
Model Classification
Family
Sundial
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
TimeBench / 1032B time points, with the model card listing THUML UTSD, LOTSA, and Chronos datasets as core upstream sources.
Recommended For
- • Probabilistic forecasting where distribution quality matters
- • Teams that want native continuous generative forecasting rather than tokenization
Strengths
- • Designed around probabilistic future generation
- • Native continuous-valued modeling rather than discrete token bins
Limitations
- • Less suited to broad non-forecasting tasks
- • Generative probabilistic focus can be overkill for simple point-forecast-only use cases
Capabilities
forecastingprobabilistic-forecastingquantile-forecastingzero-shot
Tags
sundialgenerativeprobabilisticthuml
Specifications
- Parameters
- 128M
- Architecture
- causal decoder-only transformer with TimeFlow loss / flow matching
- Context length
- 2,880
- Max context
- 2,880
- 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