gemma-4-12B-it-GGUF

Gemma-4-12B-IT

Gemma-4-12B-IT is an instruction-tuned multimodal language model developed by Google, designed to support reasoning, coding, conversational AI, and vision-language understanding workflows. This repository contains GGUF quantized variants of the model optimized for efficient local inference using llama.cpp.

The model combines strong instruction-following capabilities with native multimodal understanding, enabling users to process textual and visual information within a unified architecture. The quantized formats significantly reduce memory requirements while preserving robust multimodal reasoning and generation quality, allowing practical deployment on consumer-grade hardware.

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Model Overview

• Model Name: Gemma-4-12B-IT
• Base Model: google/gemma-4-12B-it
• Architecture: Unified encoder-free multimodal model
• Parameter Count: 12 Billion
• Context Window: 256K Tokens
• Modalities: Text, Image
• Primary Languages: Multilingual
• Developer: Google
• License: Gemma License

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Quantization Formats

This repository provides various GGUF quantized versions of the Gemma-4-12B-IT model optimized for efficient local inference using llama.cpp. Below are the details of the available quantization formats.

IQ3_M

• Size reduction of approx 75.95% (5.34 GB) compared to 16-bit (22.20 GB)
• Aggressive 3-bit quantization optimized for maximum memory efficiency
• Suitable for low-memory deployment environments and CPU-based inference
• Enables practical execution of multimodal reasoning workloads on consumer hardware
• Complex analytical reasoning, long-context processing, and fine-grained multimodal understanding may experience reduced output fidelity compared to higher-precision variants

IQ4_NL

• Size reduction of approx 70.54% (6.54 GB) compared to 16-bit (22.20 GB)
• Advanced 4-bit non-linear quantization designed to better preserve multimodal reasoning quality and generation stability
• Better suited for analytical tasks, coding workflows, structured outputs, and multimodal understanding applications
• Designed to reduce quantization loss compared to more aggressive formats
• May require slightly increased computational overhead during inference

IQ4_XS

• Size reduction of approx 71.94% (6.23 GB) compared to 16-bit (22.20 GB)
• Balanced 4-bit quantization focused on efficient inference and dependable response quality
• Provides a practical balance between memory efficiency, multimodal capability, and runtime performance
• Suitable for conversational AI, image-aware assistants, coding workflows, and structured instruction-following tasks
• Maintains stable generation quality across most practical local deployment scenarios

Q6_K

• Size reduction of approx 58.96% (9.11 GB) compared to 16-bit (22.20 GB)
• Higher-precision 6-bit K-Quant format designed to preserve reasoning capability and multimodal fidelity
• Better suited for demanding workloads involving long-context understanding, detailed image analysis, and complex problem solving
• Provides stronger output consistency and improved retention of the original model's capabilities compared to lower-bit formats
• Requires higher memory resources but offers enhanced stability and generation quality

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Training Background (Original Model)

Gemma-4-12B-IT is trained with an emphasis on multimodal understanding, instruction following, reasoning, and robust generation across a diverse range of downstream tasks.

Pretraining

• Large-scale multimodal pretraining across textual and visual datasets
• Focus on contextual understanding, cross-modal representation learning, and multilingual capability
• Optimized for downstream conversational, analytical, and multimodal reasoning workloads

Instruction Tuning

• Refined using instruction-following objectives and alignment datasets
• Enhanced for structured responses and complex task execution
• Improved consistency for reasoning, coding, multilingual interaction, and multimodal workflows

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Key Capabilities

• Multimodal Understanding Processes and integrates textual, visual information for comprehensive task understanding.

• Instruction Following Produces structured, context-aware responses aligned with user intent.

• Reasoning and Analysis Performs effectively on analytical tasks requiring multi-step problem solving.

• Coding Assistance Supports code generation, explanation, and technical reasoning workflows.

• Large Context Processing Handles long documents and extended interactions through a 256K token context window.

• Efficient Local Deployment Quantized variants enable practical multimodal inference on consumer hardware.

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Usage Example

Using llama.cpp

./llama-mtmd-cli \
  -m SandlogicTechnologies/Gemma-4-12B-IT_IQ4_NL.gguf \
  --mmproj SandlogicTechnologies/mmproj-BF16.gguf \
  --image image.png \
  -p "Analyze the diagram and explain the underlying concept step-by-step."

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Recommended Usecases

• Multimodal AI Assistants Build local assistants capable of understanding text, images inputs.

• Reasoning and Analytical Workflows Generate structured outputs for problem solving and multi-step tasks.

• Coding and Technical Assistance Support debugging, implementation guidance, and developer-oriented interactions.

• Document and Visual Understanding Analyze visual content and long-form documents using multimodal reasoning.

• Research and Experimentation Evaluate multimodal prompting strategies and local inference workflows.

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Acknowledgments

These quantized models are based on the original work by the Google DeepMind development team.

Special thanks to:

• The Google team for developing and releasing the Gemma-4-12B-IT model.

• Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

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Contact

For questions, feedback, or support, please reach out at [email protected] or visit https://www.sandlogic.com/