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
Add figure fig_exit.png
Browse files- .gitattributes +1 -0
- paper/fig_exit.png +3 -0
.gitattributes
CHANGED
|
@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
paper/TemporalMesh_Transformer_2026.pdf filter=lfs diff=lfs merge=lfs -text
|
| 37 |
paper/fig_decay.png filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
paper/TemporalMesh_Transformer_2026.pdf filter=lfs diff=lfs merge=lfs -text
|
| 37 |
paper/fig_decay.png filter=lfs diff=lfs merge=lfs -text
|
| 38 |
+
paper/fig_exit.png filter=lfs diff=lfs merge=lfs -text
|
paper/fig_exit.png
ADDED
|
Git LFS Details
|