Text Classification
Transformers
Safetensors
qwen2
trl
reward-trainer
Generated from Trainer
text-embeddings-inference
Instructions to use lewtun/reward-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lewtun/reward-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lewtun/reward-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("lewtun/reward-model") model = AutoModelForSequenceClassification.from_pretrained("lewtun/reward-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63de53ec8aa63e77c9b6b5b3dc14d84ee8a0b051444efdf64025f312828595c7
- Size of remote file:
- 5.24 kB
- SHA256:
- ab4510b07553ccbd2a6571f114440bee62cac784cebe26b16fdff92a216434af
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