Graph Machine Learning
Transformers
Safetensors
graphs_gpt_conditioned
text-generation
biology
medical
chemistry
Instructions to use DaizeDong/GraphsGPT-1W-C with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaizeDong/GraphsGPT-1W-C with Transformers:
# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("DaizeDong/GraphsGPT-1W-C", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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license: apache-2.0
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pipeline_tag: graph-ml
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tags:
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- biology
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- medical
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- chemistry
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This is the checkpoint of ICML 2024 paper [A Graph is Worth K Words: Euclideanizing Graph using Pure Transformer](https://arxiv.org/abs/2402.02464). For more information, please check the [github page](https://github.com/DaizeDong/GraphsGPT).
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