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:
- 4792e8738ac542885cb80e1d953bca868c6f82d4bb2ac52c95ae0920e3d36ef1
- Size of remote file:
- 4.72 GB
- SHA256:
- 27842a8e17e0c18a33c8e0c756bf5afe738b91acaea9dd95fc7b1b620e8e08eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.