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:
- 1e358cbabb9c0d982daa6a4ff78c3a12bd6eb731fd044c9b383d5cba08c18ebc
- Size of remote file:
- 4.66 kB
- SHA256:
- a0d834b2aa10992bddf9a5fc056d1968364fc540f03fb12e1d7115e871321de2
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