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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6fc25a764ee9a63facf3bacd690a0c9c052d7ea2d77cffa4fc2afe579029759
- Size of remote file:
- 4.66 kB
- SHA256:
- 675ad7baaf7ffa742fe1cbe33421593cfc3ecc39b8591506637a3cfb1a79ff8b
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