Instructions to use Sangam101/prabigya_partial_trained with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sangam101/prabigya_partial_trained with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Sangam101/prabigya_partial_trained") model = AutoModelForSeq2SeqLM.from_pretrained("Sangam101/prabigya_partial_trained", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Sangam101/prabigya_partial_trained: direct link, hf CLI and curl.
- Browser
- Download file 3.71 kB
-
https://huggingface.co/Sangam101/prabigya_partial_trained/resolve/main/training_args.bin
- Command line
-
hf download hf://Sangam101/prabigya_partial_trained/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Sangam101/prabigya_partial_trained/resolve/main/training_args.bin
3.71 kB
- Xet hash:
- 9a0313595c521a47021ed2b070b3efb80a0fb84b6ce703504026352be02898a8
- Size of remote file:
- 3.71 kB
- SHA256:
- ff865ea260bcee5ee7288541532f7d0c243f40f00552f77bd2edd5a40d323b0d
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