Hugging Face Overview
Model Overview
The nvidia/Qwen3-Nemotron-32B-GenRM-Principle is a 32 billion parameter Generative Reward Model (GenRM) developed by NVIDIA, leveraging the Qwen3-32B architecture. Its core function is to evaluate the quality of LLM-generated responses based on user-specified principles, assigning a numerical reward score. A higher score indicates a greater fulfillment of the given principle.
Key Capabilities
- Principle-based Evaluation: Rates LLM responses against explicit user-defined principles (e.g., "correctness", "helpfulness").
- Reward Scoring: Outputs a single float value representing the degree of principle fulfillment.
- Benchmark Performance: Achieves 81.4% on JudgeBench and 86.2% on RM-Bench (as of Sep 24, 2025), positioning it as a leading GenRM for these benchmarks.
- Foundation: Built on the robust Qwen3-32B model, ensuring strong underlying language understanding.
Use Cases
This model is ideal for applications requiring automated evaluation of LLM outputs, particularly for:
- LLM Alignment: Fine-tuning and aligning other LLMs to adhere to specific behavioral or quality principles.
- Response Ranking: Comparing and ranking multiple LLM responses based on their adherence to desired criteria.
- Quality Assurance: Automatically assessing the quality and principle compliance of generated text in various domains like chat, math, code, and safety.