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 model.safetensors from RajuEEE/RewardModelSmallerQuestionWithTwoLabels: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/RajuEEE/RewardModelSmallerQuestionWithTwoLabels/resolve/refs%2Fpr%2F1/model.safetensors
- Command line
-
hf download hf://RajuEEE/RewardModelSmallerQuestionWithTwoLabels@refs/pr/1/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/RajuEEE/RewardModelSmallerQuestionWithTwoLabels/resolve/refs%2Fpr%2F1/model.safetensors
438 MB
- Xet hash:
- fb47db4f84afd4a4173c06210b0c36b9139a49c3a0f7feb53dcbeff25a2dce98
- Size of remote file:
- 438 MB
- SHA256:
- 88770213729638524c95c7ebfc415e498a434c7c9ccfe48cac4d7f35790576bc
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