gemma-4-E4B is a multimodal model from the gemma family, built to handle more demanding reasoning and generation tasks across both visual and textual inputs. With a larger parameter count, it offers improved reasoning depth, stronger consistency across long contexts, and better performance on complex problem-solving workloads.

The model supports multimodal interactions, allowing it to process text, images, and structured content such as long-form documents. It is designed for advanced conversational systems, agentic pipelines, and applications that require higher accuracy and structured outputs.

gemma-4-E4B is particularly suited for tasks that involve multi-step reasoning, technical workflows, and multilingual processing, while still maintaining practical efficiency for optimized deployments.

Model Overview

Model Name: gemma-4-E4B
Architecture: Decoder-only Transformer with multimodal extensions
Parameter Count: 4B parameters
Context Window: 128K tokens
Modalities: Text, Image (multimodal input support)
Primary Languages: English (with multilingual generalization)
Developer: Google
License: Apache 2.0

Quantization Details

This repository provides various GGUF quantized versions of the gemma-4-E4B model, optimized for efficient local inference using llama.cpp. Below are the details of the available I-Matrix (IQ) formats.

Quantization Formats (I-Quants)

IQ3_M (3-bit Medium)

IQ3_M prioritizes extreme compression, enabling the model to run within very tight memory constraints while still preserving essential behavior.
It represents weights at approximately 3 bits per parameter and uses importance-aware scaling to retain the most impactful components.
This format is useful for experimental setups, edge environments, or scenarios where fitting the model is the primary constraint.
Due to the aggressive reduction in precision, performance on complex reasoning, long-context tasks, and structured outputs can degrade, and reconstruction overhead may influence runtime efficiency.
Size reduction of approx 68.7% (4.39 GB) compared to 16-bit (14.02 GB)

IQ4_XS

IQ4_XS provides a compact yet capable configuration by combining 4-bit quantization with importance-driven weighting.
It maintains a practical balance between size and performance, making it suitable for a wide range of real-world applications.
This format handles conversational tasks, coding prompts, and moderate reasoning workloads effectively without requiring large memory budgets.
While internally more complex than traditional quantization, it delivers consistent generation performance once inference begins.
Size reduction of approx 66.2% (4.74 GB) compared to 16-bit (14.02 GB)

IQ4_NL

IQ4_NL introduces non-linear transformations to the quantization process, allowing it to better model variations in weight distributions.
This results in improved fidelity for critical layers, particularly in scenarios involving structured reasoning and long-form generation.
It is well-suited for higher-precision workloads such as technical explanations, analytical outputs, and agentic task execution.
The increased representational quality comes with slightly higher computational cost and marginally larger model size compared to simpler formats.
Size reduction of approx 65.4% (4.85 GB) compared to 16-bit (14.02 GB)

Training Overview

Pretraining

The model is trained on a large-scale dataset combining diverse textual corpora and multimodal data sources, enabling it to understand both language and visual information in a unified manner.

Training objectives include:

Cross-modal representation learning
Large-scale language modeling
Visual-text alignment
Contextual reasoning across long sequences

Alignment and Optimization

Post-training steps refine the model for real-world usability and instruction-following:

Instruction tuning for conversational tasks
Reinforcement learning and alignment techniques
Optimization for reasoning-heavy and agentic workflows
Enhanced multimodal grounding and response consistency

Core Capabilities

Deep multi-step reasoning Handles complex chains of thought and maintains logical consistency across extended problem-solving tasks.
Agentic workflow support Enables structured task execution, tool-like reasoning patterns, and multi-stage interactions.
High-fidelity multimodal understanding Combines visual and textual signals to produce context-aware and precise outputs.
Advanced technical generation Produces detailed, structured, and accurate responses for coding, analysis, and technical domains.
Long-context stability Maintains coherence and relevance across extended documents and multi-turn conversations.
Multilingual robustness Performs reliably across diverse languages and mixed-language inputs.

Example Usage

llama.cpp

bash

./llama-cli \
  -m SandlogicTechnologies/gemma-4-E4B_IQ4_NL.gguf / \
  -p "Explain the concept of attention in transformer models."

Recommended Use Cases

Advanced reasoning systems and analytical pipelines
Agentic AI workflows and structured task automation
Technical research assistants and domain-specific copilots
Multimodal document and data interpretation
Code generation, debugging, and system design
Long-context knowledge extraction and summarization
Educational platforms for complex problem solving
High-quality conversational agents requiring consistency and depth

Acknowledgments

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

Special thanks to:

The Google team for developing and releasing the gemma-4-E4B model.
Georgi Gerganov and the llama.cpp open-source community for enabling efficient quantization and inference via the GGUF format.

Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our Website.

SandLogicTechnologies/gemma-4-E4B-GGUF

gemma-4-E4B