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
Tensor dimension mismatch in pre-trained model (channel related)
#23 opened 17 days ago
by
zuttng
How many total images in the final CAViT-S training dataset (RxRx3 + JUMP-CRISPR/ORF)?
1
#18 opened over 1 year ago
by
spud123
JUMP-CP preprocessing
#17 opened over 1 year ago
by
jasperhyp
Channel order
1
#16 opened over 1 year ago
by
mingyulu