Instructions to use SmallLion/rewardmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SmallLion/rewardmodel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="SmallLion/rewardmodel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("SmallLion/rewardmodel") model = AutoModelForSequenceClassification.from_pretrained("SmallLion/rewardmodel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85a1d5b12045eec5c874317cd0afc17e28edd55dbcfd3b0e320e6eaab34605e9
- Size of remote file:
- 268 MB
- SHA256:
- 616c47e934501c34f9b878211faf85e4f1ecd2dcbfeeec9739a12c81d2e185db
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