Instructions to use FiveC/TADA-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FiveC/TADA-checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FiveC/TADA-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoints/BartTayFinal/training_args.bin from FiveC/TADA-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 5.97 kB
-
https://huggingface.co/FiveC/TADA-checkpoints/resolve/main/checkpoints/BartTayFinal/training_args.bin
- Command line
-
hf download hf://FiveC/TADA-checkpoints/checkpoints/BartTayFinal/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FiveC/TADA-checkpoints/resolve/main/checkpoints/BartTayFinal/training_args.bin
5.97 kB
- Xet hash:
- f7519554dba7c85f3cbb016366b0ab1f69587dd43e3c5f8f8b7f749d6522a83a
- Size of remote file:
- 5.97 kB
- SHA256:
- f19e6cc6ca5c1ac1565d59639501fbc22a7e727bc63fac9071783b236149b0a2
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