Text Classification
Transformers
PyTorch
English
mechanistic-interpretability
grokking
modular-arithmetic
transformer
TransformerLens
toy-model
Instructions to use BurnyCoder/grokking-modular-multiplication-transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BurnyCoder/grokking-modular-multiplication-transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BurnyCoder/grokking-modular-multiplication-transformer")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BurnyCoder/grokking-modular-multiplication-transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download output/attention_patterns.png from BurnyCoder/grokking-modular-multiplication-transformer: direct link, hf CLI and curl.
- Browser
- Download file 19.1 kB
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https://huggingface.co/BurnyCoder/grokking-modular-multiplication-transformer/resolve/main/output/attention_patterns.png
- Command line
-
hf download hf://BurnyCoder/grokking-modular-multiplication-transformer/output/attention_patterns.png
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curl -L -o attention_patterns.png https://huggingface.co/BurnyCoder/grokking-modular-multiplication-transformer/resolve/main/output/attention_patterns.png
19.1 kB
