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
| Field | Response | |
| :---------------------------------------------------|:---------------------------------- | |
| Model Application(s): | Conversation, Question Answering, Summarization | |
| Describe the life-critical impact (if present). | N/A | |
| Use Case Restrictions: | Abide by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) . | |
| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |