Instructions to use recursionpharma/OpenPhenom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use recursionpharma/OpenPhenom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="recursionpharma/OpenPhenom", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("recursionpharma/OpenPhenom", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- **Developed, funded, and shared by:** Recursion
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- **Model type:** Vision transformer CA-MAE
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- **Image modality:** Optimized for microscopy images from the CellPainting assay
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- **License:**
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### Model Sources
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- **Developed, funded, and shared by:** Recursion
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- **Model type:** Vision transformer CA-MAE
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- **Image modality:** Optimized for microscopy images from the CellPainting assay
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- **License:** Attribution-NonCommercial 4.0 International
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### Model Sources
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