Llama-3.3-Nemotron-70B-Select
| Property | Value |
|---|---|
| Parameter Count | 70 Billion |
| Architecture | Transformer (Llama 3.3) |
| License | NVIDIA Open Model License |
| Release Date | March 18, 2025 |
| Max Context Length | 128k tokens |
What is Llama-3.3-Nemotron-70B-Select?
Llama-3.3-Nemotron-70B-Select is a sophisticated language model that builds upon Meta's Llama-3.3-70B-Instruct foundation. This model specializes in response selection using scaled Bradley-Terry modeling, effectively identifying and selecting the most helpful AI-generated responses to user queries. As part of the Feedback-Edit Inference Time Scaling (ITS) system, it achieves remarkable performance on the Arena Hard leaderboard.
Implementation Details
The model operates on a transformer architecture with 70 billion parameters, requiring at least 2x80GB GPUs (NVIDIA Ampere or newer) for deployment. It processes text inputs with a generous 128k token context window and outputs float values representing response quality scores.
- Trained on the HelpSteer3 dataset comprising 38,459 prompts with paired responses
- Supports major NVIDIA architectures including Ampere, Hopper, and Turing
- Implements through HuggingFace Transformers library
- Utilizes bfloat16 precision for optimal performance
Core Capabilities
- Response Quality Assessment through float score outputs
- High-performance response selection using Bradley-Terry modeling
- Integration with Feedback-Edit ITS system
- Commercial-ready deployment capabilities
- Extensive context handling up to 128k tokens
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to evaluate and select the most helpful responses using scaled Bradley-Terry modeling, achieving superior performance when combined with the Feedback-Edit ITS approach. It demonstrates 93.4% accuracy on Arena Hard benchmarks as part of the complete system.
Q: What are the recommended use cases?
The model is specifically designed for users seeking to improve performance through Inference-Time-Scaling in general-domain, open-ended tasks. It excels in scenarios requiring high-quality response selection and can be effectively integrated into commercial applications.
