gemma-9B_Full_claude_4th-Q4_K_M-GGUF
| Property | Value |
|---|---|
| Model Size | 9B parameters |
| Format | GGUF (Q4_K_M quantization) |
| Original Source | omarabb315/gemma-9B_Full_claude_4th |
| Hugging Face Repo | Link |
What is gemma-9B_Full_claude_4th-Q4_K_M-GGUF?
This is a quantized version of the Gemma 9B model, specifically optimized for deployment using llama.cpp. The model has been converted to the GGUF format with Q4_K_M quantization, offering an efficient balance between model size and performance for local inference.
Implementation Details
The model has been converted using llama.cpp and GGUF-my-repo space, making it compatible with local deployment scenarios. It supports both CLI and server-based implementations through llama.cpp.
- Supports context window of 2048 tokens
- Optimized for CPU and GPU inference
- Compatible with llama.cpp's latest features
Core Capabilities
- Local inference through llama.cpp
- Both CLI and server deployment options
- Efficient memory usage through Q4_K_M quantization
- Support for various hardware configurations including CUDA
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient quantization and optimization for local deployment through llama.cpp, making it accessible for users who want to run a 9B parameter model locally with reasonable resource requirements.
Q: What are the recommended use cases?
The model is ideal for local deployment scenarios where users need a balance between model performance and resource efficiency. It's particularly well-suited for applications requiring local inference without cloud dependencies.
