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
Kian Kenyon-Dean commited on
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README.md
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**While we cannot share all the internal code we've written training and evaluation of these models, it would be very useful if interested persons could raise an Issue in this repo to inform us as to what the most useful aspects of the code for this project would be of interest to the broader community.**
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## Provided models
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**While we cannot share all the internal code we've written training and evaluation of these models, it would be very useful if interested persons could raise an Issue in this repo to inform us as to what the most useful aspects of the code for this project would be of interest to the broader community.**
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## Provided models
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We have partnered with Nvidia to host a publicly-available smaller and more flexible version of the MAE phenomics foundation model, called Phenom-Beta. Interested parties can access it directly through the Nvidia BioNemo API:
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- https://blogs.nvidia.com/blog/drug-discovery-bionemo-generative-ai/
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- https://www.youtube.com/watch?v=Gch6bX1toB0
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