Llama-3_3-Nemotron-Super-49B-v1-Q4_K_M-GGUF
| Property | Value |
|---|---|
| Parameter Count | 49 Billion |
| Model Type | GGUF Quantized Language Model |
| Original Source | nvidia/Llama-3_3-Nemotron-Super-49B |
| Quantization | Q4_K_M |
| Repository | Hugging Face |
What is Llama-3_3-Nemotron-Super-49B-v1-Q4_K_M-GGUF?
This is a converted version of the Nvidia's Llama-3 Nemotron Super model, specifically optimized for local deployment using llama.cpp. The model has been quantized using the Q4_K_M format, which provides an excellent balance between model size and performance while maintaining quality.
Implementation Details
The model utilizes the GGUF format, which is specifically designed for efficient local inference using llama.cpp. It can be deployed either through command-line interface or as a server, supporting context windows up to 2048 tokens.
- Converted from original Nvidia model using llama.cpp
- Supports both CLI and server deployment modes
- Compatible with hardware acceleration (CUDA for Nvidia GPUs)
- Optimized for memory efficiency through Q4_K_M quantization
Core Capabilities
- Large-scale language understanding and generation
- Efficient local deployment without cloud dependencies
- Flexible integration options through llama.cpp
- Support for various hardware configurations
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient quantization and optimization for local deployment, making it possible to run a 49B parameter model on consumer hardware while maintaining good performance through the Q4_K_M quantization scheme.
Q: What are the recommended use cases?
The model is particularly well-suited for local deployment scenarios where privacy and offline operation are important. It can be used for text generation, analysis, and other NLP tasks that benefit from the large-scale language understanding capabilities of the Llama-3 architecture.
