Instructions to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="DuoNeural/Gemma-4-E4B-Frontend-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("DuoNeural/Gemma-4-E4B-Frontend-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("DuoNeural/Gemma-4-E4B-Frontend-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Use Docker

docker model run hf.co/DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

LM Studio
Jan

vLLM

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/Gemma-4-E4B-Frontend-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/Gemma-4-E4B-Frontend-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

SGLang

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DuoNeural/Gemma-4-E4B-Frontend-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/Gemma-4-E4B-Frontend-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DuoNeural/Gemma-4-E4B-Frontend-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/Gemma-4-E4B-Frontend-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16
```
Unsloth Desktop

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16
```

Lemonade

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Run and chat with the model

lemonade run user.Gemma-4-E4B-Frontend-GGUF-BF16

List all available models

lemonade list

Hermes Agent

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DuoNeural/Gemma-4-E4B-Frontend-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "DuoNeural/Gemma-4-E4B-Frontend-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Gemma-4-E4B-Frontend-GGUF

File size: 4,267 Bytes

---
license: apache-2.0
base_model: google/gemma-4-E4B-it
tags:
- code
- frontend
- react
- vue
- tailwind
- gemma4
- unsloth
- qlora
- gguf
library_name: transformers
---

# 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).

---

## DuoNeural

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

| | |
|---|---|
| 🤗 HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
| 🐙 GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) |
| 🐦 X / Twitter | [@DuoNeural](https://x.com/DuoNeural) |
| 📧 Email | duoneural@proton.me |
| 📬 Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) |
| ☕ Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) |
| 🌐 Site | [duoneural.com](https://duoneural.com) |

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

*Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).*

### DuoNeural Research Publications

| Title | DOI |
|-------|-----|
| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |

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