Graph Machine Learning
Transformers
Safetensors
graphs_gpt
text-generation
biology
medical
chemistry
Instructions to use DaizeDong/GraphsGPT-2W with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaizeDong/GraphsGPT-2W with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DaizeDong/GraphsGPT-2W", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| pipeline_tag: graph-ml | |
| tags: | |
| - biology | |
| - medical | |
| - chemistry | |
| 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). | |