Instructions to use RajuEEE/RewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModel") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RajuEEE/RewardModel: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/RajuEEE/RewardModel/resolve/main/training_args.bin
- Command line
-
hf download hf://RajuEEE/RewardModel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RajuEEE/RewardModel/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- 75ce4a1dfa215a34042ddec47a62a5da8102edcfead285b4097964a6d419da70
- Size of remote file:
- 4.03 kB
- SHA256:
- 5b23ab3d5da3ed88439d0f86ca9de87aeecefa7433058c994f706261dddfc270
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