Gemma-3-4B-IT GGUF
| Property | Value |
|---|---|
| Model Size | 4B parameters |
| Context Length | 128K tokens |
| Author | Google DeepMind (GGUF by Mungert) |
| License | Refer to Google's Terms of Use |
| Supported Tasks | Text Generation, Image Understanding |
What is gemma-3-4b-it-gguf?
Gemma-3-4b-it-gguf is a GGUF-formatted version of Google's Gemma 3 instruction-tuned model, designed for efficient deployment across various hardware configurations. This multimodal model can process both text and images, generating coherent text responses with a substantial 128K token context window.
Implementation Details
The model is available in multiple quantization formats to accommodate different hardware capabilities and memory constraints. These range from high-precision BF16 and F16 formats to more compressed Q4_K, Q6_K, and Q8 variants, enabling deployment on everything from high-end GPUs to resource-constrained CPUs.
- BF16/F16 variants for maximum accuracy and GPU acceleration
- Q4_K variants for minimal memory usage (ideal for CPU inference)
- Q6_K and Q8 variants offering balanced performance and accuracy
- Multimodal support with 896x896 image resolution processing
Core Capabilities
- Text generation and completion tasks
- Image understanding and analysis
- Multilingual support (140+ languages)
- Long context handling (128K tokens)
- Question answering and summarization
- Visual reasoning and description
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient GGUF format implementation and variety of quantization options, making it highly accessible for different deployment scenarios. It combines multimodal capabilities with a large context window in a relatively compact 4B parameter size.
Q: What are the recommended use cases?
The model is well-suited for applications requiring both text and image understanding, including content generation, image analysis, document summarization, and question answering. The various quantization options make it particularly valuable for deployment in resource-constrained environments or edge devices.
