Instructions to use li-jay-cs/test2-rlhf-rm-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use li-jay-cs/test2-rlhf-rm-checkpoint with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("li-jay-cs/test2-rlhf-rm-checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from li-jay-cs/test2-rlhf-rm-checkpoint: direct link, hf CLI and curl.
- Browser
- Download file 575 MB
-
https://huggingface.co/li-jay-cs/test2-rlhf-rm-checkpoint/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://li-jay-cs/test2-rlhf-rm-checkpoint/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/li-jay-cs/test2-rlhf-rm-checkpoint/resolve/main/pytorch_model.bin
575 MB
- Xet hash:
- 928435e063115bf957dbcdef2b063530f2145c8ccfb5ce5da75c3ce5a325ba18
- Size of remote file:
- 575 MB
- SHA256:
- 7eb52d0210f0364cb3478c0d3f0f0317054aa5ba68b9a495e83ebadf48b0df4d
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