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
| [build-system] | |
| requires = ["setuptools >= 61.0"] | |
| build-backend = "setuptools.build_meta" | |
| [project] | |
| name = "maes_microscopy_project" | |
| version = "0.1.0" | |
| authors = [ | |
| {name = "kian-kd", email = "kian.kd@recursionpharma.com"}, | |
| {name = "Laksh47", email = "laksh.arumugam@recursionpharma.com"}, | |
| ] | |
| requires-python = ">=3.10.4" | |
| dependencies = [ | |
| "huggingface-hub", | |
| "timm", | |
| "torch>=2.3", | |
| "torchmetrics", | |
| "torchvision", | |
| "tqdm", | |
| "transformers", | |
| "xformers", | |
| "zarr", | |
| "pytorch-lightning>=2.1", | |
| "matplotlib", | |
| "scikit-image", | |
| "ipykernel", | |
| "isort", | |
| "ruff", | |
| "pytest", | |
| ] | |
| [tool.setuptools] | |
| py-modules = [] |