Text Classification
Transformers
Safetensors
English
qwen2
reward-model
3b
RLHF
text-embeddings-inference
Instructions to use kanishkez/Reward-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanishkez/Reward-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kanishkez/Reward-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanishkez/Reward-Model") model = AutoModelForSequenceClassification.from_pretrained("kanishkez/Reward-Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a9076790badc25ed842f0a49f094346391eacce632deb2e0907374a8489cbf7
- Size of remote file:
- 5.39 kB
- SHA256:
- ec0f502254c34f28b51c92bc5371ad5ef32475f5d350ca5000cc7aa5c1f803de
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