Mungert / GELab-Zero-4B-preview-GGUF

GELab-Zero-4B-preview GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 7f8ef50cc .

Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

Model Details

This model is part of the GELab-Zero project, which aims to accelerate the innovation and application deployment of GUI Agents by providing:

1. A 4B GUI Agent model capable of running on local computers.
2. Plug-and-play inference infrastructure that handles ADB connections, dependency installation, and task recording/replay ( available in the GELab-Zero ).

Key Capabilities

• Local Deployment : Optimized for consumer-grade hardware, balancing low latency with privacy.
• GUI Navigation : Proficient in detecting and interacting with UI elements (click, type, slide, wait, etc.) based on visual cues.
• Complex Task Execution : Handles multi-step long-horizon tasks across various apps (Food, Transportation, Shopping, Social, etc.).
• Open-World Generalization : Capable of zero-shot operation across diverse unseen applications and complex dynamic interfaces without requiring app-specific adaptation.

Usage

Quick Start with Ollama

The easiest way to run inference is using Ollama.

1. Install Ollama : Download from ollama.com .

2. Download the Model :

# Install huggingface-cli
pip install huggingface_hub
# Download model
huggingface-cli download --resume-download stepfun-ai/GELab-Zero-4B-preview --local-dir gelab-zero-4b-preview

3. Create and Run in Ollama :

cd gelab-zero-4b-preview
ollama create gelab-zero-4b-preview -f Modelfile
    
# Test the model
curl -X POST http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
        "model": "gelab-zero-4b-preview",
        "messages": [{"role": "user", "content": "Hello, GELab-Zero!"}]
      }'

To use this model for actual Android device control (ADB connection, task execution), please use the GELab-Zero .

Citation

If you find GELab-Zero-4B-preview useful for your research, please consider citing our work :)

@software{gelab_zero_2025,
  title={GELab-Zero: An Advanced Mobile Agent Inference System},
  author={GELab Team},
  year={2025},
  url={https://github.com/stepfun-ai/gelab-zero}
}

@inproceedings{gelab_mt_rl,
  title={GUI Exploration Lab: Enhancing Screen Navigation in Agents via Multi-Turn Reinforcement Learning},
  author={Yan, Haolong and Shen, Yeqing and Huang, Xin and Wang, Jia and Tan, Kaijun and Liang, Zhixuan and Li, Hongxin and Ge, Zheng and Yoshie, Osamu and Li, Si and others},
  booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems}
}

🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks :

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor . You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test :
Choose an AI assistant type :

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring , specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs ) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI , let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test :

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source . Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊