Feature Extraction
Transformers
Safetensors
meshgraphnet
graph-neural-network
cfd
cylinder-flow
custom_code
Instructions to use ketiswp/meshgraphnet-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ketiswp/meshgraphnet-hf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ketiswp/meshgraphnet-hf", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ketiswp/meshgraphnet-hf", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ketiswp/meshgraphnet-hf: direct link, hf CLI and curl.
- Browser
- Download file 542 Bytes
-
https://huggingface.co/ketiswp/meshgraphnet-hf/resolve/main/config.json
- Command line
-
hf download hf://ketiswp/meshgraphnet-hf/config.json
-
curl -L -o config.json https://huggingface.co/ketiswp/meshgraphnet-hf/resolve/main/config.json
542 Bytes
| { | |
| "architectures": [ | |
| "MeshGraphNetForSimulation" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "meshgraphnet.MeshGraphNetConfig", | |
| "AutoModel": "meshgraphnet.MeshGraphNetForSimulation" | |
| }, | |
| "edge_input_size": 3, | |
| "hidden_size": 128, | |
| "layer_norm_eps": 1e-05, | |
| "model_type": "meshgraphnet", | |
| "normalizer_eps": 1e-08, | |
| "num_hidden_layers": 15, | |
| "num_mlp_hidden_layers": 2, | |
| "num_node_types": 9, | |
| "output_size": 2, | |
| "split_first_linear": false, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.55.4", | |
| "velocity_size": 2 | |
| } | |