Instructions to use berkeley-nest/Starling-RM-7B-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use berkeley-nest/Starling-RM-7B-alpha with Transformers:
# Load model directly from transformers import AutoTokenizer, LLMForSequenceRegression tokenizer = AutoTokenizer.from_pretrained("berkeley-nest/Starling-RM-7B-alpha") model = LLMForSequenceRegression.from_pretrained("berkeley-nest/Starling-RM-7B-alpha") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 46cf01d6e993f0d06fc76b1ace4f40018851930297f6800b1db9fbcf37e4ff9f
- Size of remote file:
- 26.7 GB
- SHA256:
- e822738b1730aee4bcd4695d25836907dd3b98dff1ac112260d89c2085c0a743
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