Instructions to use RajuEEE/RewardModelOnlyOnAnswer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModelOnlyOnAnswer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModelOnlyOnAnswer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModelOnlyOnAnswer") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModelOnlyOnAnswer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RajuEEE/RewardModelOnlyOnAnswer: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/RajuEEE/RewardModelOnlyOnAnswer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RajuEEE/RewardModelOnlyOnAnswer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RajuEEE/RewardModelOnlyOnAnswer/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 0a7b0e58450fd1a759654257949889ff1e050d2d01ac63b65c3634c7166bd7f9
- Size of remote file:
- 438 MB
- SHA256:
- 8aab8850acf22ca01bb158ff55e4bc8242100cc6a2766330df54d94a7fd4358b
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