Kairos-10M
onlinemldi-lab/Kairos_10m9.95M params | 2K context | $0.00025 per forecast
Kairos-10M is the smallest public checkpoint in the Kairos family, developed by researchers from Ant Group and ShanghaiTech University. The official model cards and project page describe adaptive tokenization plus instance-adaptive rotary position encoding to better handle heterogeneous time-series structure and varying local information density. It is the lightest-weight way to access the Kairos design while staying in the zero-shot forecasting regime.
Model Classification
Family
Kairos
Type
time series foundation model
Pretrained time-series model exposed on TSFM.ai for zero-shot or few-shot forecasting workloads.
Resources
Training Data
PreSTS corpus with 300B+ time points, as documented by the official Kairos model cards and project page.
Recommended For
- • Adaptive zero-shot forecasting across heterogeneous series
- • Teams that want a mid-size open family with modern tokenization ideas
Strengths
- • Adaptive tokenization handles changing information density well
- • Clear parameter-size ladder from small to larger public checkpoints
Limitations
- • Newer family with a smaller production footprint than the most established lines
- • Focused on forecasting rather than general multi-task time-series tooling
Capabilities
Tags
Specifications
- Parameters
- 9.95M
- Architecture
- encoder-decoder transformer with adaptive patching and instance-adaptive rotary position encoding
- Context length
- 2,048
- Max context
- 2,048
- 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