🧠 Super-Qwen-7B (NeuroBridge Distilled)

Super-Qwen is a series of models fine-tuned via Asymmetric Distillation using the NeuroBridge framework.

This model is designed to run on resource-constrained edge hardware (like HFT nodes and private mesh networks) while maintaining high-fidelity architectural and coding reasoning.

🚀 Key Features

• Personal Distillation: Designed to be fine-tuned on your own private coding traces.
• Workflow Capture: The NeuroBridge framework supports self-supervised learning from your professional intellectual workflow.
• Format: GGUF (Optimized for Ollama/Llama.cpp).

📊 Evaluation Results (Base + Distillation)

Benchmark	Score
Coding	88%
Safety	100%
Hallucination	50%

🛠 Usage with NeuroBridge

To use this model in your local NeuroBridge instance:

1. Download the .gguf adapter.
2. Place it in your adapters/ directory.
3. Run:

neuro train promote my_adapter.gguf

🧠 Philosophy: Asymmetric Scaling

We believe that edge intelligence should not be a "miniature" version of the cloud, but a specialized distilled version. Super-Qwen models are built to perform specific, high-value tasks with zero network dependency.

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Developed by: seyominaoto Framework: NeuroBridge