Instructions to use tuhink/hacking-rewards-harmless-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuhink/hacking-rewards-harmless-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhink/hacking-rewards-harmless-train")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tuhink/hacking-rewards-harmless-train") model = AutoModelForSequenceClassification.from_pretrained("tuhink/hacking-rewards-harmless-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from tuhink/hacking-rewards-harmless-train: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/tuhink/hacking-rewards-harmless-train/resolve/main/training_args.bin
- Command line
-
hf download hf://tuhink/hacking-rewards-harmless-train/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tuhink/hacking-rewards-harmless-train/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 611254b8f9c2abbb52ac43b7cca89a0a33fcc59c2f5953a1cbc1ec22b794f281
- Size of remote file:
- 5.37 kB
- SHA256:
- 93a55d03f0904f95948c2eb77c66df906fcd6b201b011d9e96c7821cf51dc0ca
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