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:
- 8083f4c249faa07e07c345fe6eac2f1c87c4b3102abb729a634361215882263a
- Size of remote file:
- 4.47 GB
- SHA256:
- 343bef8d71084a8b0faeca049232e8aadf3e4e5c1824cb9d549b6b7a115641e6
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