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-theme/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-theme/training_args.bin
- Command line
-
hf download hf://FiveC/TADA-checkpoints/checkpoints/BartTayFinal-theme/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/FiveC/TADA-checkpoints/resolve/main/checkpoints/BartTayFinal-theme/training_args.bin
5.97 kB
- Xet hash:
- 2ea75ef8056cd1374f28b6642f9f31248aa75c69ace1f4e9e3f8dd0bb72b0164
- Size of remote file:
- 5.97 kB
- SHA256:
- 4a43ac093b5b90c9df4cf46974a90f9c5d0f44ff5d4b1238e04d54bae66c421c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.