Instructions to use hsali/testmodel1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsali/testmodel1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hsali/testmodel1") model = AutoModel.from_pretrained("hsali/testmodel1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from hsali/testmodel1: direct link, hf CLI and curl.
- Browser
- Download file 3.52 kB
-
https://huggingface.co/hsali/testmodel1/resolve/main/training_args.bin
- Command line
-
hf download hf://hsali/testmodel1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/hsali/testmodel1/resolve/main/training_args.bin
3.52 kB
- Xet hash:
- 7e48720dc95c89533b93323b66a30a50c6027c334ff717305ea77d869fe9aa6a
- Size of remote file:
- 3.52 kB
- SHA256:
- 2603b426587a5b3643b2e9320fe122d13c3712ffa89102996ac5db1eb3422d81
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.