Instructions to use Akhil06042002/model_model_super_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akhil06042002/model_model_super_model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Akhil06042002/model_model_super_model") model = AutoModelForMultipleChoice.from_pretrained("Akhil06042002/model_model_super_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Akhil06042002/model_model_super_model: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Akhil06042002/model_model_super_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Akhil06042002/model_model_super_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Akhil06042002/model_model_super_model/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 4001338c6d0e69f00568d3c7fab782b1237f2fdb368131073cc5c40a1522130c
- Size of remote file:
- 3.58 kB
- SHA256:
- 5c8551e8a19df134d9454d98c81288cbb317eeccbab2bf8c8f6f4823c94a7932
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.