Holo-3.1-4B GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 263cc04a5.

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?

Holo3.1: Fast & Local Computer Use Agents

Model Description

Holo3.1 is our latest family of Vision-Language Models (VLMs) for computer use agents. Building on Holo3, it expands support beyond browser and desktop automation to mobile environments, introduces native function-calling support for seamless integration with agent frameworks, and enables local deployment through optimized quantized checkpoints.

The Holo3.1 family spans model sizes from 0.8B to 35B-A3B parameters. Across computer use, UI grounding, mobile automation, and business workflows, Holo3.1 delivers strong performance while improving deployment flexibility and cost efficiency.

• Developed by: H Company
• Model type: Vision-Language Models for Navigation and Computer Use Agents
• Available models: Holo3.1-0.8B, Holo3.1-4B, Holo3.1-9B, Holo3.1-35B-A3B
• Base models: Qwen 3.5 family
• Supported environments: Web, Desktop, Mobile
• Available quantizations for Holo3.1-35B-A3B: BF16, FP8, NVFP4, Q4 GGUF
• Blog Post: hcompany.ai/holo3.1
• Quickstart: hub.hcompany.ai/quickstart
• License: Apache 2.0 License

Performance vs Cost

The figure below compares the overall performance and inference cost of the Holo3.1 and Qwen 3.5 families. Overall performance averages computer use, mobile automation, enterprise workflows, and UI grounding benchmarks.

Holo3.1 establishes a strong Pareto frontier across model sizes, from lightweight local agents to state-of-the-art enterprise deployments.

Benchmark Results

Holo3.1 delivers strong performance across computer use, mobile automation, enterprise workflows, and UI grounding benchmarks.

Table 1: Evaluation results across computer use, mobile automation, enterprise workflows, and grounding benchmarks.

Get Started

Explore our Quickstart guide to learn how to integrate Holo3.1 into your applications, deploy local agents, or run optimized inference on NVIDIA hardware.

Citation

@misc{hai2026holo31,
      title={Holo3.1: Fast & Local Computer Use Agents},
      author={H Company},
      year={2026},
      url={https://huggingface.co/Hcompany/Holo3.1-35B-A3B},
}

<!--End Original Model Card-->

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# <span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

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

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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](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/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](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) 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](https://github.com/Mungert69). Feel free to use whatever you find helpful.

If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. 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! 😊