Instructions to use MMattaparthy/sft_rewardmodel_final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MMattaparthy/sft_rewardmodel_final with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="MMattaparthy/sft_rewardmodel_final")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MMattaparthy/sft_rewardmodel_final") model = AutoModelForSequenceClassification.from_pretrained("MMattaparthy/sft_rewardmodel_final") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c435a7c2fce3740aac9be9d4baec48b0eb52e597b5e006811ccad8e9789ccdc
- Size of remote file:
- 6.03 kB
- SHA256:
- 42e0f7bd6a9b4ac0438041839da49be75d09ce59ae00987578bd88e38b213335
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