Instructions to use RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 2c73d13d4d3f405e2ccc361fd4ddbff075928f5afdad5d37bd996816b81761e8
- Size of remote file:
- 438 MB
- SHA256:
- 2cf9f4eda581eea9438b9162c63ed00d8b7a0d04f12348235df83f0ba772d224
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