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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from huggingface_mae import MAEModel
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huggingface_phenombeta_model_dir = "."
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# huggingface_modelpath = "recursionpharma/
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@pytest.fixture
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def huggingface_model():
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# Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/test-pb-model to this directory
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# huggingface-cli download recursionpharma/
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huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
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huggingface_model.eval()
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return huggingface_model
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from huggingface_mae import MAEModel
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huggingface_phenombeta_model_dir = "."
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# huggingface_modelpath = "recursionpharma/OpenPhenom"
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@pytest.fixture
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def huggingface_model():
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# Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/test-pb-model to this directory
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# huggingface-cli download recursionpharma/OpenPhenom --local-dir=.
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huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
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huggingface_model.eval()
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return huggingface_model
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