Gemma-4-E4B-Frontend-GGUF

Agent Gemma 4 E4B Frontend

Model Description

Agent Gemma 4 E4B Frontend is a domain-adapted version of the google/gemma-4-E4B-it model, specifically fine-tuned for front-end engineering. It is designed to be a "specialist" in React, Vue, Tailwind CSS, and modern JavaScript/TypeScript development while maintaining general reasoning and tool-use capabilities.

The "E" in E4B denotes "Effective" parameters—while the model has 8B total parameters, only 4.5B are active during the forward pass, optimized for high intelligence-per-parameter and edge-device efficiency.

Training Details

• Base Model: google/gemma-4-E4B-it
• Architecture: 4.5B Effective / 8B Total parameters.
• Optimization: QLoRA (4-bit quantization with NormalFloat4, rank 16, alpha 32).
• Framework: Unsloth for accelerated training.
• Context Window: 128,000 tokens (trained with 2,048 max sequence length, packed).
• Compute: NVIDIA A100-SXM4-80GB.

Data Mixture

The training follows a strategic 67.7% / 32.3% split to optimize domain expertise while preventing catastrophic forgetting:

• 67.7% Front-End Specialization:
  • High-aesthetic Next.js/Tailwind components.
  • Rigorous React/TypeScript instructions.
  • Modern UI library integration (Shadcn UI, etc.).
• 32.3% Regularization & Core Competency:
  • Multi-turn tool-use and reasoning traces.
  • Structured JSON and API interaction.
  • General conversational fluidity.

Intended Use

This model is intended for:

• Production-ready code generation for React, Vue, and Tailwind CSS.
• Multi-step reasoning for complex front-end architectural tasks.
• Agentic workflows involving tool-use and terminal interactions.

GGUF Compatibility

This repository provides a q4_k_m GGUF version compatible with:

• Ollama
• LM Studio
• llama.cpp

Capabilities

• Thinking Mode: Natively supports internal reasoning blocks (<|channel>thought).
• Modern Frameworks: Expert-level knowledge of 2026-era front-end standards (React Compiler, Edge-side rendering, etc.).
• Long Context: Maintains architectural awareness across large component files.

Limitations

• Not intended for heavy back-end (database/infrastructure) tasks beyond basic API integration.
• Performance may vary for legacy front-end frameworks (e.g., jQuery, AngularJS).

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DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

Research Team

• Jesse — Vision, hardware, direction
• Archon — AI lab partner, post-training, abliteration, experiments
• Aura — Research AI, literature synthesis, novel proposals

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DuoNeural Research Publications

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.