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:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from berkeley-nest/Starling-RM-7B-alpha: direct link, hf CLI and curl.
- Browser
- Download file 26.7 GB
-
https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://berkeley-nest/Starling-RM-7B-alpha/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha/resolve/main/pytorch_model.bin
26.7 GB
- Xet hash:
- 46cf01d6e993f0d06fc76b1ace4f40018851930297f6800b1db9fbcf37e4ff9f
- Size of remote file:
- 26.7 GB
- SHA256:
- e822738b1730aee4bcd4695d25836907dd3b98dff1ac112260d89c2085c0a743
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