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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): d2f383a
Update config.json
Browse files- config.json +1 -1
config.json
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{
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"_name_or_path": "
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"architectures": [
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"BertForSequenceClassification"
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"_name_or_path": "nicholasKluge/RewardModel",
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"architectures": [
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"BertForSequenceClassification"
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