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
Update test_huggingface_mae.py
Browse files- test_huggingface_mae.py +2 -2
test_huggingface_mae.py
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@@ -3,7 +3,7 @@ import torch
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from huggingface_mae import MAEModel
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-
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# huggingface_modelpath = "recursionpharma/test-pb-model"
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@@ -11,7 +11,7 @@ huggingface_phenombeta_model_dir = "."
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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/test-pb-model --local-dir=.
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huggingface_model = MAEModel.from_pretrained(
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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_openphenom_model_dir = "."
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# huggingface_modelpath = "recursionpharma/test-pb-model"
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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/test-pb-model --local-dir=.
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huggingface_model = MAEModel.from_pretrained(huggingface_openphenom_model_dir)
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huggingface_model.eval()
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return huggingface_model
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