Llama-3.1-Nemotron-Nano-8B-v1-GGUF
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| Release Date | March 18, 2025 |
| License | NVIDIA Open Model License |
| Context Length | 128K tokens |
| Paper | Reward-aware Preference Optimization |
What is Llama-3.1-Nemotron-Nano-8B-v1-GGUF?
This model is a sophisticated derivative of Meta's Llama-3.1-8B-Instruct, specifically enhanced through multi-phase post-training to excel in reasoning, human chat preferences, and specialized tasks like RAG and tool calling. It represents a careful balance between model accuracy and computational efficiency, capable of running on a single RTX GPU.
Implementation Details
The model comes in multiple quantization formats (BF16, F16, Q4_K, Q6_K, Q8_0, etc.) to accommodate different hardware configurations and memory constraints. It features a dense decoder-only Transformer architecture and supports a remarkable context length of 128K tokens.
- Multiple quantization options for different hardware setups
- Supports both reasoning ON/OFF modes via system prompts
- Optimized for various tasks including math, code, and general reasoning
- Multi-language support including English and several other languages
Core Capabilities
- Advanced reasoning and chat functionality
- High performance in math and coding tasks (95.4% pass@1 on MATH500 with reasoning enabled)
- Extensive context length handling (131,072 tokens)
- Flexible deployment options from high-end GPUs to memory-constrained environments
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its ability to switch between detailed reasoning and direct response modes, combined with its extensive quantization options for different hardware configurations. It achieves impressive benchmark scores while maintaining deployment flexibility.
Q: What are the recommended use cases?
This model is ideal for developers building AI agents, chatbots, and RAG systems. It's particularly well-suited for applications requiring strong reasoning capabilities, code generation, and math problem-solving, while being deployable on consumer-grade hardware.
