Instructions to use JacobLinCool/RhythmGenGRU-1-hard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JacobLinCool/RhythmGenGRU-1-hard with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JacobLinCool/RhythmGenGRU-1-hard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download sample_data/mnist_train_small.csv from JacobLinCool/RhythmGenGRU-1-hard: direct link, hf CLI and curl.
- Browser
- Download file 36.5 MB
-
https://huggingface.co/JacobLinCool/RhythmGenGRU-1-hard/resolve/refs%2Fpr%2F1/sample_data/mnist_train_small.csv
- Command line
-
hf download hf://JacobLinCool/RhythmGenGRU-1-hard@refs/pr/1/sample_data/mnist_train_small.csv
-
curl -L -o mnist_train_small.csv https://huggingface.co/JacobLinCool/RhythmGenGRU-1-hard/resolve/refs%2Fpr%2F1/sample_data/mnist_train_small.csv
36.5 MB
- Xet hash:
- 7abf407e9f8c5c70bc35de579582b5ba02a7ede091389676b21fe7305042e068
- Size of remote file:
- 36.5 MB
- SHA256:
- 1ef64781aa03180f4f5ce504314f058f5d0227277df86060473d973cf43b033e
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