Instructions to use Jumpr/HF_compatibility_testv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jumpr/HF_compatibility_testv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Jumpr/HF_compatibility_testv2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jumpr/HF_compatibility_testv2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PretrainedConfig | |
| class LightningTransformerModelConfig(PretrainedConfig): | |
| model_type = "lightning_transformer" | |
| def __init__(self, cfg=None, **kwargs): | |
| self.cfg = cfg | |
| super().__init__(**kwargs) | |