Gemma-3-1b-it-gguf
| Property | Value |
|---|---|
| Author | Google DeepMind |
| Model Size | 3B parameters |
| Context Length | 32K tokens input, 8192 tokens output |
| License | See Terms of Use |
What is gemma-3-1b-it-gguf?
Gemma-3-1b-it-gguf is a GGUF-formatted version of Google's Gemma 3B Instruct model, built using the same technology that powers their Gemini models. This implementation offers multiple quantization options to accommodate different hardware capabilities and memory constraints, making it highly versatile for various deployment scenarios.
Implementation Details
The model is available in several formats, including BF16, F16, and various quantized versions (Q4_K, Q6_K, Q8). Each version is optimized for different use cases, from high-precision inference on capable hardware to efficient operation on memory-constrained devices.
- BF16/F16 variants for maximum accuracy and hardware acceleration
- Q4_K through Q8 quantized versions for efficient CPU inference
- Special hybrid versions combining different precision levels for optimal performance
Core Capabilities
- Text generation and instruction following
- 32K token input context window
- 8192 token output generation
- Optimized for various hardware configurations
- Multiple quantization options for different memory/performance trade-offs
Frequently Asked Questions
Q: What makes this model unique?
This implementation stands out for its extensive range of quantization options, allowing users to choose the perfect balance between model accuracy and resource usage. It's particularly notable for maintaining good performance even in highly compressed formats.
Q: What are the recommended use cases?
The model is ideal for text generation tasks requiring a balance of performance and efficiency. The BF16/F16 versions are recommended for high-performance systems, while the quantized versions (Q4_K through Q8) are perfect for deployment on resource-constrained devices or CPU-only environments.
