Llama-3.1-Nemotron-Nano-8B-v1
| Property | Value |
|---|---|
| Developer | NVIDIA |
| Base Model | Meta Llama 3.1 8B Instruct |
| Context Length | 128K tokens |
| License | NVIDIA Open Model License |
| Release Date | March 18, 2025 |
| Paper | Reward-aware Preference Optimization |
What is Llama-3.1-Nemotron-Nano-8B-v1?
Llama-3.1-Nemotron-Nano-8B-v1 is a powerful 8B parameter language model that builds upon Meta's Llama 3.1 architecture. This model stands out for its exceptional balance between performance and efficiency, featuring enhanced reasoning capabilities and the ability to run on a single RTX GPU. The model underwent extensive post-training optimization using both supervised fine-tuning and reinforcement learning techniques, particularly focusing on mathematics, coding, reasoning, and tool-calling capabilities.
Implementation Details
The model employs a dense decoder-only Transformer architecture and supports an impressive context length of 128K tokens. It features two distinct operational modes - "Reasoning On" and "Reasoning Off" - controlled via system prompts, allowing users to optimize for different use cases. The implementation supports BF16 precision and is compatible with NVIDIA's Hopper and Ampere architectures.
- Multi-phase post-training process including SFT and RL stages
- Integrated with REINFORCE and Online Reward-aware Preference Optimization
- Supports multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
- Optimized for both local deployment and cloud infrastructure
Core Capabilities
- Advanced reasoning and mathematical problem-solving (95.4% pass rate on MATH500 with reasoning enabled)
- Strong performance in MT-Bench evaluation (8.1 score with reasoning on)
- Exceptional code generation capabilities (84.6% pass rate on MBPP 0-shot)
- Effective tool calling and RAG system integration
- High instruction-following accuracy (up to 82.1% on IFEval)
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its dual-mode operation (Reasoning On/Off) combined with its efficiency in running on a single GPU while maintaining high performance. The extensive post-training optimization and 128K context window make it particularly suitable for complex reasoning tasks and practical applications.
Q: What are the recommended use cases?
The model is ideal for developing AI agents, chatbots, RAG systems, and applications requiring strong reasoning capabilities. It's particularly well-suited for mathematical problem-solving, code generation, and multi-lingual applications where efficient compute resource usage is crucial.
