Text Classification
Transformers
PyTorch
Safetensors
English
bert
reward model
alignment
preference model
RLHF
text-embeddings-inference
Instructions to use nicholasKluge/RewardModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nicholasKluge/RewardModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nicholasKluge/RewardModel")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/RewardModel") model = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/RewardModel") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#2
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6462d34f66884837c26ff77873b81c8e89db0bceb892f1fe08c146cabbc5f808
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size 433267692
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