Instructions to use Shabdobhedi/aragpt2_base_small_finetune_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Shabdobhedi/aragpt2_base_small_finetune_1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Shabdobhedi/aragpt2_base_small_finetune_1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Shabdobhedi/aragpt2_base_small_finetune_1: direct link, hf CLI and curl.
- Browser
- Download file 5.62 kB
-
https://huggingface.co/Shabdobhedi/aragpt2_base_small_finetune_1/resolve/main/training_args.bin
- Command line
-
hf download hf://Shabdobhedi/aragpt2_base_small_finetune_1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Shabdobhedi/aragpt2_base_small_finetune_1/resolve/main/training_args.bin
5.62 kB
- Xet hash:
- c30b898a70e11227209671ad8eaa63ed674a50ab6f8fadd96e802771d1939e95
- Size of remote file:
- 5.62 kB
- SHA256:
- c913cb47b2a62c87307eb77036b639c4407d96195796bd847f528f18fa1cd291
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.