google-gemma-3-12b-it-qat-q4_0-gguf-small
| Property | Value |
|---|---|
| Model Size | 6.89 GB |
| Author | stduhpf |
| Model Type | Quantized Language Model |
| Source | Hugging Face |
What is google-gemma-3-12b-it-qat-q4_0-gguf-small?
This model is an optimized version of Google's Gemma 3B language model, created through a strategic merger of Google's QAT weights and Bartowski's quantized embeddings. It achieves an optimal balance between model size and performance, featuring a unique quantization approach that reduces memory footprint while maintaining high accuracy.
Implementation Details
The model implements a hybrid quantization strategy, combining Q4_0 quantization with optimized embedding tables. Unlike the original Google QAT weights that use fp16 for embeddings, this version utilizes calibrated quantized embeddings from Bartowski's implementation, resulting in significant memory savings without compromising performance.
- File size: 6.89 GB (smaller than standard Q4_0 implementations)
- Perplexity score: 9.2637 ±0.07216 on wiki.text.raw
- Uses static quantization instead of dynamic imatrix-based approach
Core Capabilities
- Efficient memory usage while maintaining performance comparable to larger models
- Improved perplexity metrics compared to similar quantized versions
- Optimized for production deployment with reduced storage requirements
- Balanced trade-off between model size and accuracy
Frequently Asked Questions
Q: What makes this model unique?
This model achieves better performance metrics than standard Q4_0 quantization while requiring less storage space (6.89 GB vs 8.07 GB for Google's QAT Q4_0), primarily through its innovative approach to embedding table quantization.
Q: What are the recommended use cases?
This model is ideal for deployments where memory efficiency is crucial but performance cannot be compromised. It's particularly suitable for production environments where the standard Gemma model would be too resource-intensive.
