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
File size: 173 Bytes
6877289 | 1 2 3 4 5 6 7 8 | import torch
class Normalizer(torch.nn.Module):
def forward(self, pixels: torch.Tensor) -> torch.Tensor:
pixels = pixels.float()
return pixels / 255.0
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