Graph Machine Learning
BioNeMo
molecular-property-prediction
admet
drug-discovery
graph-transformer
cheminformatics
Instructions to use nvidia/NV-KERMT-70M-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BioNeMo
How to use nvidia/NV-KERMT-70M-v2 with BioNeMo:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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Download privacy.md from nvidia/NV-KERMT-70M-v2: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
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https://huggingface.co/nvidia/NV-KERMT-70M-v2/resolve/main/privacy.md
- Command line
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hf download hf://nvidia/NV-KERMT-70M-v2/privacy.md
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curl -L -o privacy.md https://huggingface.co/nvidia/NV-KERMT-70M-v2/resolve/main/privacy.md
1.54 kB
| Field | Response |
|---|---|
| Generatable or reverse engineerable personal data? | No |
| Personal data used to create this model? | No |
| Was consent obtained for any personal data used? | Not Applicable |
| How often is dataset reviewed? | Before Release |
| Was data from user interactions with the AI model (e.g. user input and prompts) used to train the model? | No |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data |
| Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |