Transformers
PyTorch
English
language-model
graph-attention
adaptive-depth
temporal-decay
efficient-llm
Eval Results (legacy)
Instructions to use vigneshwar234/TemporalMesh-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vigneshwar234/TemporalMesh-Transformer with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vigneshwar234/TemporalMesh-Transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download paper/fig_training.png from vigneshwar234/TemporalMesh-Transformer: direct link, hf CLI and curl.
- Browser
- Download file 180 kB
-
https://huggingface.co/vigneshwar234/TemporalMesh-Transformer/resolve/main/paper/fig_training.png
- Command line
-
hf download hf://vigneshwar234/TemporalMesh-Transformer/paper/fig_training.png
-
curl -L -o fig_training.png https://huggingface.co/vigneshwar234/TemporalMesh-Transformer/resolve/main/paper/fig_training.png
180 kB

- Xet hash:
- 873c2b7fc19a58223cc2a6b180d8f17b912b29a7bd2f124601703238833a1156
- Size of remote file:
- 180 kB
- SHA256:
- e9b18c58c5c94159c4fd32bcf6d69348b91fec7a6031b22f163a54e994f6f50d
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