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
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 |
|---|---|---|
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 |
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💻 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