Graph Machine Learning
Transformers
Safetensors
mamba
chemistry
drug-discovery
molecular-modeling
mumo
text-generation-inference
Instructions to use zihaojing/MuMo-Pretrained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zihaojing/MuMo-Pretrained with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zihaojing/MuMo-Pretrained", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Add pipeline tag, library name, paper link, GitHub link, and BibTeX citation
#1
by nielsr HF Staff - opened
This PR enhances the model card by:
- Adding
pipeline_tag: graph-mlfor better discoverability, as the model performs molecular representation learning involving graph structures. - Specifying
library_name: transformersto enable the automated "how to use" widget, given the model's compatibility with the Hugging Face Transformers library (as evidenced byconfig.jsonand usage snippets). - Including a direct link to the paper: Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning at the top.
- Adding a direct link to the official GitHub repository: https://github.com/selmiss/MuMo for easy access to the code.
- Updating the "Training script" detail to link directly to the GitHub repository.
- Incorporating the full BibTeX citation provided in the GitHub README for proper academic attribution.
Please review and merge if these improvements align with your goals.
zihaojing changed pull request status to merged