Gemma-3-12B-IT-INT4-AWQ
| Property | Value |
|---|---|
| Model Size | 12B parameters (INT4 quantized) |
| Model Type | Multimodal Instruction-tuned LLM |
| Context Window | 128K tokens |
| Training Data | 12 trillion tokens |
| Author | Google DeepMind (original) / gaunernst (quantized) |
What is gemma-3-12b-it-int4-awq?
This model is a quantized version of Google's Gemma 3 12B instruction-tuned model, converted to 4-bit integers (INT4) format using AWQ quantization. It maintains the powerful capabilities of the original model while significantly reducing its computational requirements and memory footprint, making it more accessible for deployment on consumer hardware.
Implementation Details
The model represents a carefully optimized version of the original Gemma architecture, converted from Flax checkpoints to HuggingFace format with INT4 quantization. It supports both text and image inputs with a substantial 128K token context window, enabling processing of lengthy documents and complex multimodal tasks.
- Supports over 140 languages
- Optimized for efficient deployment while maintaining model quality
- Compatible with HuggingFace Transformers ecosystem
- Handles both text generation and image understanding tasks
Core Capabilities
- Text generation and summarization
- Image analysis and visual question answering
- Multilingual support across 140+ languages
- Code generation and understanding
- Mathematical reasoning and problem-solving
Frequently Asked Questions
Q: What makes this model unique?
This model stands out by offering the capabilities of Google's Gemma 3 architecture in a highly efficient INT4 quantized format, making it accessible for deployment on consumer hardware while maintaining strong performance across a wide range of tasks.
Q: What are the recommended use cases?
The model excels in content creation, chatbots, text summarization, image analysis, research applications, and educational tools. It's particularly well-suited for scenarios where computational efficiency is crucial while maintaining high-quality output.
