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 requirements.txt
Browse files- requirements.txt +27 -0
requirements.txt
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# Core
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torch>=2.2.0
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torchvision
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torchaudio
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# TMT-specific
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einops>=0.7.0
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torch-geometric
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# NLP / data
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transformers>=4.40.0
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datasets>=2.19.0
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tokenizers>=0.19.0
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# Experiment tooling
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wandb>=0.17.0
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tensorboard>=2.16.0
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torchviz
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matplotlib>=3.8.0
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jupyter>=1.0.0
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pandas>=2.2.0
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tabulate>=0.9.0
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# Dev
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pytest>=8.0.0
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black>=24.0.0
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ruff>=0.4.0
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