Instructions to use bradmin/reward-gpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bradmin/reward-gpt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bradmin/reward-gpt")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bradmin/reward-gpt") model = AutoModelForSequenceClassification.from_pretrained("bradmin/reward-gpt") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ec3884ff97c98330178ee507838d54827b53829b6f9bea564fcd4293fafd744
- Size of remote file:
- 5.08 GB
- SHA256:
- 7aac08b0078cb2fdd9289144e195da94484338161874b90d5245b7839daa3213
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