Instructions to use wltjr1007/LEAR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wltjr1007/LEAR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="wltjr1007/LEAR", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("wltjr1007/LEAR", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class ConditionalUNetConfig(PretrainedConfig): | |
| model_type = "conditional-unet" | |
| def __init__( | |
| self, | |
| encoder_rep="evanrsl/resnet-Alzheimer", | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.encoder_rep = encoder_rep |