PLLuM-12B-instruct-Q5_K_M-GGUF
| Property | Value |
|---|---|
| Model Size | 12B parameters |
| Format | GGUF (llama.cpp compatible) |
| Quantization | Q5_K_M |
| Source Model | CYFRAGOVPL/PLLuM-12B-instruct |
| Repository | Hugging Face |
What is PLLuM-12B-instruct-Q5_K_M-GGUF?
PLLuM-12B-instruct-Q5_K_M-GGUF is a quantized version of the PLLuM-12B instruction-tuned language model, specifically optimized for local deployment using llama.cpp. This version features Q5_K_M quantization, offering an efficient balance between model size and performance.
Implementation Details
The model has been converted from the original CYFRAGOVPL/PLLuM-12B-instruct to the GGUF format, making it compatible with llama.cpp's ecosystem. It can be deployed using either the CLI or server mode, supporting context windows up to 2048 tokens.
- Optimized for llama.cpp implementation
- Q5_K_M quantization for efficient memory usage
- Supports both CLI and server deployment options
- Compatible with various hardware configurations including GPU acceleration
Core Capabilities
- Local inference without cloud dependencies
- Instruction-following capabilities
- Flexible deployment options via llama.cpp
- Support for various hardware acceleration options (CUDA, Metal, etc.)
Frequently Asked Questions
Q: What makes this model unique?
This model combines the capabilities of the PLLuM-12B instruction-tuned model with efficient Q5_K_M quantization, making it suitable for local deployment while maintaining good performance characteristics.
Q: What are the recommended use cases?
The model is ideal for users who need to run a capable language model locally, particularly in scenarios where privacy, offline access, or custom deployment configurations are required. It's well-suited for both CLI-based applications and server deployments.
