Time-MoE-50M
onlineMaple728/TimeMoE-50M0.1B total / 50M active params | 4K context | $0.00025 per forecast | Apache-2.0
Time-MoE-50M is the smaller public checkpoint in Xiaohongshu's Time-MoE family. The family naming refers to its active-parameter regime, but the published checkpoint stores a larger total parameter count because inactive experts remain part of the release. It is the lightest public way to evaluate the sparse Time-MoE design.
Model Classification
Family
TimeMoE
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
Time-300B, a multi-domain corpus spanning more than nine domains and over 300B time points, per the official paper and repository.
Recommended For
- • Long-context forecasting with sparse-expert scaling
- • Teams exploring MoE behavior in time-series foundation models
Strengths
- • Sparse experts offer large-capacity behavior without a fully dense footprint
- • Well aligned to long-context autoregressive forecasting
Limitations
- • MoE operational behavior can be less familiar than dense baselines
- • Not the best first pick if you just need a simple compact deployment
Capabilities
forecastingzero-shothigh-throughput
Tags
timemoemoeautoregressivecost-efficient
Specifications
- Parameters
- 0.1B total / 50M active
- Architecture
- decoder-only transformer with sparse Mixture-of-Experts routing
- Context length
- 4,096
- Max context
- 4,096
- 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
- Apache-2.0
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