Gemma Coder 9B GGUF
| Property | Value |
|---|---|
| Author | mradermacher |
| Base Model | TeamDelta/gemma_coder_9b |
| Format | GGUF with various quantizations |
| Model URL | https://huggingface.co/mradermacher/gemma_coder_9b-i1-GGUF |
What is gemma_coder_9b-i1-GGUF?
This is a specialized quantized version of the Gemma Coder 9B model, optimized for efficient deployment while maintaining performance. It offers multiple quantization options ranging from 2.5GB to 7.7GB, allowing users to balance between model size, inference speed, and quality based on their specific needs.
Implementation Details
The model implements various quantization techniques, including both standard and IQ (improved quantization) variants. The quantization options range from IQ1_S (2.5GB) to Q6_K (7.7GB), with IQ-quants generally offering better performance than similarly-sized standard quants.
- Multiple quantization options (IQ1, IQ2, IQ3, IQ4, Q4, Q5, Q6)
- Size ranges from 2.5GB to 7.7GB
- Optimized weight matrices using imatrix technology
- Compatible with standard GGUF loaders
Core Capabilities
- Efficient model deployment with minimal quality loss
- Flexible size options for different hardware constraints
- Optimized for coding-related tasks
- Q4_K_M (5.9GB) recommended for balanced performance
- Q6_K (7.7GB) offers near-original model quality
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its comprehensive range of quantization options, particularly the IQ variants that offer better quality at smaller sizes compared to traditional quantization methods. The availability of multiple size options makes it highly adaptable to different deployment scenarios.
Q: What are the recommended use cases?
For optimal performance, the Q4_K_M (5.9GB) variant is recommended as it offers a good balance of speed and quality. For maximum quality, the Q6_K variant provides performance practically identical to the static quantized version. Lower-size options are available for resource-constrained environments.
