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 training_args.bin from berkeley-nest/Starling-RM-7B-alpha: direct link, hf CLI and curl.
- Browser
- Download file 5.31 kB
-
https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha/resolve/main/training_args.bin
- Command line
-
hf download hf://berkeley-nest/Starling-RM-7B-alpha/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha/resolve/main/training_args.bin
5.31 kB
- Xet hash:
- 3bdf4845ea786db780312b0cf677f5b970b33218e89a7fe0270f2f6b330db89c
- Size of remote file:
- 5.31 kB
- SHA256:
- b0970cf0f8a8b2c721397d2b59e62220a6e0b2228225ba1537abeaf5fd367de8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.