Instructions to use Abhinit/HW2-reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Abhinit/HW2-reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Abhinit/HW2-reward")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Abhinit/HW2-reward") model = AutoModelForSequenceClassification.from_pretrained("Abhinit/HW2-reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Abhinit/HW2-reward: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Abhinit/HW2-reward/resolve/main/training_args.bin
- Command line
-
hf download hf://Abhinit/HW2-reward/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Abhinit/HW2-reward/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 51f648a5f1a08bfe5d5275125b769e29d8bfac3c4cb07504f320fed6e9889f87
- Size of remote file:
- 5.37 kB
- SHA256:
- a5ffcbaacd46faf2fac6d999d77314b46f916282b1c4ac5cfdc7b0fdf9c501e5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.