Instructions to use Ray2333/gpt2-large-helpful-reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ray2333/gpt2-large-helpful-reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ray2333/gpt2-large-helpful-reward_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ray2333/gpt2-large-helpful-reward_model") model = AutoModelForSequenceClassification.from_pretrained("Ray2333/gpt2-large-helpful-reward_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7bb35d0660ec69571aaa1ee1acfa70766130dcc006ed15e738da76207d48c06b
- Size of remote file:
- 3.1 GB
- SHA256:
- 7e79f03e20870598bf264a18f7453c97e991f3538576b6cfb78aee52cedd44ef
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