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-SwitchOut/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-SwitchOut/training_args.bin
- Command line
-
hf download hf://FiveC/TADA-checkpoints/checkpoints/BartTayFinal-SwitchOut/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FiveC/TADA-checkpoints/resolve/main/checkpoints/BartTayFinal-SwitchOut/training_args.bin
5.97 kB
- Xet hash:
- a87ec7804c49859963205122334e31747c62d74b8fe8aac26bf250a418f740e4
- Size of remote file:
- 5.97 kB
- SHA256:
- 4beff2251f6b0b247a817baef9e5c064bf51d0d0027483bfe750f0446ce5f396
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