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 pytorch_model.bin from RajuEEE/RewardModelSmallerQuestionWithTwoLabels: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/RajuEEE/RewardModelSmallerQuestionWithTwoLabels/resolve/refs%2Fpr%2F2/pytorch_model.bin
- Command line
-
hf download hf://RajuEEE/RewardModelSmallerQuestionWithTwoLabels@refs/pr/2/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/RajuEEE/RewardModelSmallerQuestionWithTwoLabels/resolve/refs%2Fpr%2F2/pytorch_model.bin
438 MB
- Xet hash:
- ed152e98dfbdfa632d8804f64b0a3a7525013d01543d1e0deb499c7500892c6c
- Size of remote file:
- 438 MB
- SHA256:
- fb28efcd62b4716a480161719154e282e256ed72a01ef35d5a7ef8e0ea7120c6
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