Feature Extraction
Transformers
Safetensors
mettle
computational-pathology
histopathology
foundation-model
scanner-robustness
custom_code
Instructions to use slideflow-labs/mettle-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use slideflow-labs/mettle-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="slideflow-labs/mettle-1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("slideflow-labs/mettle-1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __init__.py from slideflow-labs/mettle-1: direct link, hf CLI and curl.
- Browser
- Download file 212 Bytes
-
https://huggingface.co/slideflow-labs/mettle-1/resolve/main/__init__.py
- Command line
-
hf download hf://slideflow-labs/mettle-1/__init__.py
-
curl -L -o __init__.py https://huggingface.co/slideflow-labs/mettle-1/resolve/main/__init__.py
212 Bytes
| """Mettle Hugging Face custom model.""" | |
| from .configuration_mettle import MettleConfig | |
| from .modeling_mettle import MettleModel, MettleModelOutput | |
| __all__ = ["MettleConfig", "MettleModel", "MettleModelOutput"] | |