Instructions to use RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer") model = AutoModelForSequenceClassification.from_pretrained("RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer/resolve/main/training_args.bin
- Command line
-
hf download hf://RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/RajuEEE/RewardModel_TwoLabels_OnlyOnAnswer/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 7eba478301228af601c7f6e16848e174b04132feeb0cb0b6cb7db324e0f80f5f
- Size of remote file:
- 4.09 kB
- SHA256:
- b85e6c94bf6bcda93ac17cc780435f6aa9b5cbec46341ba6675af67d3fa620d8
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