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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zihaojing/MuMo-Pretrained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add pipeline tag, library name, paper link, GitHub link, and BibTeX citation
Verified signatureThis PR enhances the model card by: - Adding `pipeline_tag: graph-ml` for better discoverability, as the model performs molecular representation learning involving graph structures. - Specifying `library_name: transformers` to enable the automated "how to use" widget, given the model's compatibility with the Hugging Face Transformers library (as evidenced by `config.json` and usage snippets). - Including a direct link to the paper: [Structure-Aware Fusion with Progressive Injection for Multimodal Molecular Representation Learning](https://huggingface.co/papers/2510.23640) 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.8f687c3 -
v1.0 - Initial release
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initial commit
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