YingLong 300M

online

qcw2333/YingLong_300m

300M params | 4K context | $0.00025 per forecast | CC-BY-4.0

YingLong 300M is the largest checkpoint in the YingLong family, offering the highest forecast quality at the cost of increased latency. The official paper frames it as the top-capacity YingLong release for zero-shot forecasting, while the broader family continues to use direct quantile-style probabilistic outputs rather than a language-model-style decoder. Best suited for workloads where accuracy is the primary concern.

Model Classification

Family

YingLong

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

Official model card states that the released YingLong checkpoints were pre-trained on 78B time points.

Recommended For

• Dense probabilistic forecasting with fine-grained quantile coverage
• Workloads that need richer distribution coverage than standard low-count quantile sets

Strengths

• Quantile-focused output head provides unusually dense probabilistic coverage
• Clear parameter-size ladder from 6M to 300M for cost-accuracy tradeoffs

Limitations

• Newer family with less public benchmark coverage than the most established TSFMs
• Dense quantile output increases per-token cost compared to point-forecast-only models

Capabilities

forecastingquantile-forecastingzero-shotlong-horizon

Specifications

Parameters: 300M
Architecture: non-causal transformer forecaster with multi-quantile output head
Context length: 4,096
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: CC-BY-4.0

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