Selene-1-Mini-Llama-3.1-8B-Q6_K-GGUF
| Property | Value |
|---|---|
| Model Size | 8B parameters |
| Format | GGUF (Q6_K quantization) |
| Author | NikolayKozloff |
| Original Source | AtlaAI/Selene-1-Mini-Llama-3.1-8B |
| Hugging Face Repository | Link |
What is Selene-1-Mini-Llama-3.1-8B-Q6_K-GGUF?
This is a quantized version of the Selene-1-Mini-Llama model, specifically optimized for efficient local inference using llama.cpp. The model has been converted to the GGUF format with Q6_K quantization, providing an excellent balance between model size and performance.
Implementation Details
The model leverages the GGUF format, which is the successor to GGML, offering improved efficiency and compatibility with llama.cpp. The Q6_K quantization scheme allows for reduced memory usage while maintaining good performance characteristics.
- Converted from original Selene-1-Mini-Llama using llama.cpp
- Implements Q6_K quantization for optimal size/performance ratio
- Compatible with both CLI and server implementations
- Supports context window of 2048 tokens
Core Capabilities
- Local inference through llama.cpp
- Efficient memory usage through quantization
- Command-line and server deployment options
- Direct integration with Hugging Face repositories
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for local deployment through llama.cpp, using the efficient GGUF format with Q6_K quantization, making it accessible for users who want to run large language models locally with reasonable hardware requirements.
Q: What are the recommended use cases?
The model is ideal for local deployment scenarios where users need to run inference without cloud dependencies. It's particularly suitable for applications requiring a balance between performance and resource usage, with support for both CLI and server implementations.
