Instructions to use Jumpr/HF_compatibility_testv3-AutoModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jumpr/HF_compatibility_testv3-AutoModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jumpr/HF_compatibility_testv3-AutoModel", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jumpr/HF_compatibility_testv3-AutoModel", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PreTrainedModel | |
| from .configuration_lightningtransformer import LightningTransformerModelConfig | |
| from .lightningtransformer import LightningTransformer | |
| class LightningTransformerModel(PreTrainedModel): | |
| config_class = LightningTransformerModelConfig | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.model = LightningTransformer(**config.cfg) | |
| self.post_init() | |