Feature Extraction
Transformers
Safetensors
English
symtime
time series
forecasting
foundation models
pretrained models
generative models
time series foundation models
custom_code
Instructions to use FlowVortex/SymTime with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlowVortex/SymTime with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="FlowVortex/SymTime", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FlowVortex/SymTime", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from FlowVortex/SymTime: direct link, hf CLI and curl.
- Browser
- Download file 300 Bytes
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https://huggingface.co/FlowVortex/SymTime/resolve/62d10283b589f25a2f7898f99d2c2c8700b46063/README.md
- Command line
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hf download hf://FlowVortex/SymTime@62d10283b589f25a2f7898f99d2c2c8700b46063/README.md
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curl -L -o README.md https://huggingface.co/FlowVortex/SymTime/resolve/62d10283b589f25a2f7898f99d2c2c8700b46063/README.md
300 Bytes
metadata
license: apache-2.0
metrics:
- mse
- mae
tags:
- time series
- forecasting
- foundation models
- pretrained models
- generative models
- time series foundation models
library_name: transformers
SymTime-NeurIPS2025-Huggingface
The pipeline and model config of SymTime model for Huggingface