Instructions to use RajuEEE/RewardModelSmallerQuestionWithTwoLabels with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModelSmallerQuestionWithTwoLabels with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModelSmallerQuestionWithTwoLabels")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModelSmallerQuestionWithTwoLabels") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModelSmallerQuestionWithTwoLabels", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RajuEEE/RewardModelSmallerQuestionWithTwoLabels: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/RajuEEE/RewardModelSmallerQuestionWithTwoLabels/resolve/main/training_args.bin
- Command line
-
hf download hf://RajuEEE/RewardModelSmallerQuestionWithTwoLabels/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RajuEEE/RewardModelSmallerQuestionWithTwoLabels/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 6c70a030abd00ee6d6949c0778e4dac6efe4ba6fbebbb2331637cedb287cf594
- Size of remote file:
- 4.09 kB
- SHA256:
- a10b23651e2b766841ad75d4653f70d94ea0c455c2f80782c05eb4e1aafde5b3
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