Instructions to use radia/scenery_reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use radia/scenery_reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="radia/scenery_reward_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("radia/scenery_reward_model") model = AutoModelForSequenceClassification.from_pretrained("radia/scenery_reward_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from radia/scenery_reward_model: direct link, hf CLI and curl.
- Browser
- Download file 5.84 kB
-
https://huggingface.co/radia/scenery_reward_model/resolve/main/training_args.bin
- Command line
-
hf download hf://radia/scenery_reward_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/radia/scenery_reward_model/resolve/main/training_args.bin
5.84 kB
- Xet hash:
- edcb07bb6680c0b8779bbf0d2101146c2d26d98749dae591eebfba8e8545eecc
- Size of remote file:
- 5.84 kB
- SHA256:
- f413568bf2e39335633ec950ade3a026b94083a8cbcd27117d6bc1ef981704f8
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