Instructions to use Khurram123/SigmaMath-Visual-Core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Khurram123/SigmaMath-Visual-Core with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Khurram123/SigmaMath-Visual-Core",
	filename="qwen_math_q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Khurram123/SigmaMath-Visual-Core with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M

Use Docker

docker model run hf.co/Khurram123/SigmaMath-Visual-Core:Q4_K_M

LM Studio
Jan
Ollama
How to use Khurram123/SigmaMath-Visual-Core with Ollama:
```
ollama run hf.co/Khurram123/SigmaMath-Visual-Core:Q4_K_M
```

Unsloth Studio new

How to use Khurram123/SigmaMath-Visual-Core with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Khurram123/SigmaMath-Visual-Core to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Khurram123/SigmaMath-Visual-Core to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Khurram123/SigmaMath-Visual-Core to start chatting

Pi new

How to use Khurram123/SigmaMath-Visual-Core with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SigmaMath-Visual-Core"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Khurram123/SigmaMath-Visual-Core with Docker Model Runner:
```
docker model run hf.co/Khurram123/SigmaMath-Visual-Core:Q4_K_M
```

Lemonade

How to use Khurram123/SigmaMath-Visual-Core with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Khurram123/SigmaMath-Visual-Core:Q4_K_M

Run and chat with the model

lemonade run user.SigmaMath-Visual-Core-Q4_K_M

List all available models

lemonade list

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

Downloads last month: 228

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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