Polaris-4B-Preview-GGUF

<span style="color: #7FFF7F;">Polaris-4B-Preview GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit 73e53dc8.

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<span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

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?

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<a href="https://readyforquantum.com/huggingface_gguf_selection_guide.html" style="color: #7FFF7F;"> Click here to get info on choosing the right GGUF model format </a>

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<!--Begin Original Model Card--> <div align="center"> <h1> POLARIS </h1> <div> 🌠 A <strong>PO</strong>st-training recipe for scaling R<strong>L</strong> on <strong>A</strong>dvanced <strong>R</strong>eason<strong>I</strong>ng model<strong>S</strong> 🚀 </div> </div> <br> <div align="center" style="line-height: 1;"> <a href="https://github.com/ChenxinAn-fdu/POLARIS" style="margin: 2px;"> <img alt="Code" src="https://img.shields.io/badge/POLARIS-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://honorable-payment-890.notion.site/POLARIS-A-POst-training-recipe-for-scaling-reinforcement-Learning-on-Advanced-ReasonIng-modelS-1dfa954ff7c38094923ec7772bf447a1" target="_blank" style="margin: 2px;"> <img alt="Blog" src="https://img.shields.io/badge/Notion-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://x.com/AnChancy46881/status/1936099024769368506" style="margin: 2px;"> <img alt="Twitter" src="https://img.shields.io/badge/Twitter-white?style=for-the-badge&logo=twitter&logoColor=000&color=000&labelColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="commingsoon" style="margin: 2px;"> <img alt="Paper" src="https://img.shields.io/badge/Paper-%23000000.svg?style=for-the-badge&logo=arxiv&logoColor=000&color=000&labelColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div>

Overview

Polaris is an open-source post-training method that uses reinforcement learning (RL) scaling to refine and enhance models with advanced reasoning abilities. Our research shows that even top-tier models like Qwen3-4B can achieve significant improvements on challenging reasoning tasks when optimized with Polaris. By leveraging open-source data and academic-level resources, Polaris pushes the capabilities of open-recipe reasoning models to unprecedented heights. In benchmark tests, our method even surpasses top commercial systems, including Claude-4-Opus, Grok-3-Beta, and o3-mini-high (2025/01/03).

Polaris's Recipe

• Data Difficulty: Before training, Polaris analyzes and maps the distribution of data difficulty. The dataset should not be overwhelmed by either overly difficult or trivially easy problems. We recommend using a data distribution with a slight bias toward challenging problems, which typically exhibits a mirrored J-shaped distribution.
• Diversity-Based Rollout: We leverage the diversity among rollouts to initialize the sampling temperature, which is then progressively increased throughout the RL training stages.
• Inference-Time Length: Polaris incorporates length extrapolation techniques for generating longer CoT at inference stage. This enables a "train-short, generate-long" paradigm for CoT reasoning, mitigating the computational burden of training with excessively long rollouts .
• Exploration Efficiency: Exploration efficiency in Polaris is enhanced through multi-stage training. However, reducing the model's response length in the first stage poses potential risks. A more conservative approach would be to directly allow the model to "think longer" from the beginning.

The details of our training recipe and analysis can be found in our blog post. The code and data for reproducing our results can be found in our github repo.

Evaluation Results

Acknowledgements

The training and evaluation codebase is heavily built on Verl. The reward function in polaris in from DeepScaleR. Our model is trained on top of Qwen3-4B and DeepSeek-R1-Distill-Qwen-7B. Thanks for their wonderful work.

Citation

@misc{Polaris2025,
    title = {POLARIS: A Post-Training Recipe for Scaling Reinforcement Learning on Advanced Reasoning Models},
    url = {https://hkunlp.github.io/blog/2025/Polaris},
    author = {An, Chenxin and Xie, Zhihui and Li, Xiaonan and Li, Lei and Zhang, Jun and Gong, Shansan and Zhong, Ming and Xu, Jingjing and Qiu, Xipeng and Wang, Mingxuan and Kong, Lingpeng}
    year = {2025}
}

<!--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:

👉 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! 😊