Google Gemma-3B-it GGUF
| Property | Value |
|---|---|
| Original Model | google/gemma-3b-it |
| Quantization Types | Multiple (Q2 to Q8_0) |
| Vision Capability | Yes (with MMPROJ) |
| Author | bartowski |
What is google_gemma-3-4b-it-GGUF?
This is a comprehensive collection of GGUF quantizations of Google's Gemma-3B-it model, optimized for different hardware configurations and use cases. The model features various compression levels ranging from 1.54GB to 7.77GB, including specialized formats for ARM and AVX architectures.
Implementation Details
The model utilizes llama.cpp's advanced quantization techniques, featuring both traditional K-quants and newer I-quants. It includes vision capabilities through MMPROJ support, requiring additional vision-specific files for image processing tasks.
- Multiple quantization options from Q2_K to Q8_0
- Specialized formats for different hardware architectures
- Vision support through dedicated MMPROJ files
- Online weight repacking for optimized performance
Core Capabilities
- Text generation with different quality-size tradeoffs
- Vision processing capabilities
- Optimized performance on various hardware configurations
- Support for both CPU and GPU inference
Frequently Asked Questions
Q: What makes this model unique?
This implementation offers an extensive range of quantization options, allowing users to choose the perfect balance between model size and performance for their specific hardware. It also includes vision capabilities, making it versatile for multimodal applications.
Q: What are the recommended use cases?
For maximum quality, the Q6_K_L or Q8_0 variants are recommended. For balanced performance, Q4_K_M is suggested as the default choice. For systems with limited RAM, the I-quants (IQ3_M, IQ4_XS) offer good performance at smaller sizes.
