Instructions to use RajuEEE/RewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModel") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RajuEEE/RewardModel: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/RajuEEE/RewardModel/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RajuEEE/RewardModel/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RajuEEE/RewardModel/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a94f80753febb9374517feb5275d12edb005c368ec82e125a0af64fd7b7b0897
- Size of remote file:
- 438 MB
- SHA256:
- 40be10dc1f25ed21cf437c365453f348ab2104fca187de1ce23e7983e4b746a8
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