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
by zuttng - opened
Hello, I have a question. Do the weights in the pre-trained model only support six-channel images? My four-channel image input results in a 1025-dimensional image (4×256+1), but the model tells me the expected dimension is 1537 (6×256+1). Does the pre-trained model support the parameter "channel_agnostic"?
Thank you!