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 README.md
Browse files
README.md
CHANGED
|
@@ -73,7 +73,7 @@ huggingface_phenombeta_model_dir = "."
|
|
| 73 |
|
| 74 |
@pytest.fixture
|
| 75 |
def huggingface_model():
|
| 76 |
-
# Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/
|
| 77 |
# huggingface-cli download recursionpharma/OpenPhenom --local-dir=.
|
| 78 |
huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
|
| 79 |
huggingface_model.eval()
|
|
|
|
| 73 |
|
| 74 |
@pytest.fixture
|
| 75 |
def huggingface_model():
|
| 76 |
+
# Make sure you have the model/config downloaded from https://huggingface.co/recursionpharma/OpenPhenom to this directory
|
| 77 |
# huggingface-cli download recursionpharma/OpenPhenom --local-dir=.
|
| 78 |
huggingface_model = MAEModel.from_pretrained(huggingface_phenombeta_model_dir)
|
| 79 |
huggingface_model.eval()
|