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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FlowVortex/SymTime", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "SymTimeModel" | |
| ], | |
| "_name_or_path": "FlowVortex/SymTime", | |
| "auto_map": { | |
| "AutoConfig": "configuration_symtime.SymTimeConfig", | |
| "AutoModel": "model.SymTimeModel" | |
| }, | |
| "patch_size": 16, | |
| "stride": 16, | |
| "num_layers": 6, | |
| "d_model": 512, | |
| "d_ff": 2048, | |
| "num_heads": 8, | |
| "norm": "BatchNorm", | |
| "dropout": 0.1, | |
| "act": "gelu", | |
| "pre_norm": false, | |
| "initializer_factor": 0.05, | |
| "model_type": "symtime", | |
| "torch_dtype": "float32", | |
| "transformers_version": "5.5.4" | |
| } |