Text Classification
Transformers
Safetensors
English
qwen2
nvidia
qwen2.5
reward-model
text-embeddings-inference
Instructions to use nvidia/Qwen-2.5-Nemotron-32B-Reward with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/Qwen-2.5-Nemotron-32B-Reward with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nvidia/Qwen-2.5-Nemotron-32B-Reward")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nvidia/Qwen-2.5-Nemotron-32B-Reward") model = AutoModelForSequenceClassification.from_pretrained("nvidia/Qwen-2.5-Nemotron-32B-Reward", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9a9b9b18b748b0f0f4cd4ad9df07c2b9088c77cc2e00ef2d72914446ce42d3f3
- Size of remote file:
- 4.72 GB
- SHA256:
- 457e6b1494f37e026f179d75fc02a872439ef93ed8848a9bc35b6f8c0adeae98
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