| --- |
| license: mit |
| library_name: gguf |
| base_model: Qwen/Qwen2.5-Coder-7B |
| datasets: |
| - AI-MO/NuminaMath-TIR |
| tags: |
| - mathematics |
| - geogebra |
| - 3d-visualization |
| - education |
| - coding |
| - reasoning |
| - uvicorn |
| - fastapi |
| --- |
| |
| <p align="center"> |
| <img src="logo.png" alt="ΣMath Visual Core v2.0 Logo" width="550"/> |
| </p> |
|
|
| # ΣMath — Visual Computation Engine v2.0 |
|
|
| ### **Powered by Qwen2.5-Coder-7B & NuminaMath-TIR** |
|
|
| **Developed by: Khurram Pervez, Assistant Professor of Mathematics** |
|
|
| **ΣMath Core** is a high-performance mathematical visualization engine that bridges the gap between deep symbolic reasoning and real-time interactive rendering. By leveraging a fine-tuned **Qwen2.5-Coder-7B** backbone with the **NuminaMath-TIR** dataset, the model excels at **Chain-of-Thought (CoT)** reasoning, allowing it to solve complex geometric problems before translating them into interactive code. |
|
|
| The engine utilizes a specialized **Resilient Execution Pipeline** to render 3D manifolds, animations, and parametric surfaces directly in the browser, optimized specifically for local deployment on NVIDIA hardware. |
|
|
| ## 🚀 The Multi-Stage Pipeline |
|
|
| ### 1. TIR (Thought-Intermediate-Reasoning) |
| By training on the **NuminaMath-TIR** dataset, the model follows a rigorous logical path: |
| * **Identification:** Analyzes the geometric properties of the requested manifold. |
| * **Calculation:** Determines the necessary vertices, normals, and parametric equations. |
| * **Code Synthesis:** Generates high-efficiency Python code (Plotly/Matplotlib) using its native **Coder** capabilities. |
|
|
| ### 2. The Resilient Engine (FastAPI Layer) |
| To ensure stability during research, the system includes a proprietary processing layer: |
| * **Dummy Interception:** Captures and silences `plt.show()` commands to prevent GUI thread blocking on Ubuntu/Linux servers. |
| * **Colorscale Transpilation:** Automatically maps Matplotlib colormap names (e.g., *spring, summer*) to Plotly-valid equivalents to ensure 3D renders never fail. |
| * **Sandbox Execution:** Executes generated code in a safe local scope using your **RTX 4060 Ti**. |
|
|
| ## 📸 Interactive Visual Samples |
|
|
| Here are examples of advanced parametric surfaces generated in real-time by **ΣMath Core v2.0**, showcasing the full **Thought-Intermediate-Reasoning (TIR)** pipeline. |
|
|
| | 3D Torus Visualization | Full Research Dashboard Interface | Resilient Color Scaling Error Fix | |
| | :---: | :---: | :---: | |
| | <img src="viz.png" alt="ΣMath Interactive Torus" width="100%"/> | <img src="dashboard.png" alt="ΣMath Dashboard" width="100%"/> | <img src="fix.png" alt="Resilient Colorscale Error" width="100%"/> | |
|
|
| ## 💻 System Configuration |
|
|
| | Component | Specification | |
| | :--- | :--- | |
| | **Compute Engine** | NVIDIA GeForce RTX 4060 Ti (16GB VRAM) | |
| | **Model Format** | GGUF (Quantized Q4_K_M) | |
| | **Context Window** | n_ctx=4096 (Optimized for detailed manifold calculation) | |
| | **OS** | Ubuntu 22.04 LTS (Optimized for `Agg` Backend) | |
| | **Frameworks** | FastAPI, Llama-cpp-python, Plotly, mpld3 | |
| |
| ## 🛠️ Quick Start |
| |
| ### 1. Installation |
| ```bash |
| # Clone this repository |
| git clone [https://huggingface.co/Khurram123/SigmaMath-Visual-Core](https://huggingface.co/Khurram123/SigmaMath-Visual-Core) |
| cd SigmaMath-Visual-Core |
| |
| # Install dependencies |
| pip install fastapi uvicorn llama-cpp-python numpy matplotlib mpld3 plotly |
