Khurram123/SigmaMath-Visual-Core

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

💻 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

# 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