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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

Llama new
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 Desktop

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 serve -hf Khurram123/SigmaMath-Visual-Core:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Khurram123/SigmaMath-Visual-Core:Q4_K_M"
        }
      ]
    }
  }
}

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

Hermes Agent

How to use Khurram123/SigmaMath-Visual-Core with Hermes Agent:

Start the llama.cpp server

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

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Khurram123/SigmaMath-Visual-Core:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Khurram123/SigmaMath-Visual-Core:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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: 26

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Khurram123/SigmaMath-Visual-Core

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Quantized

(51)

this model

Khurram123
/

SigmaMath-Visual-Core

ΣMath — Visual Computation Engine v2.0

Powered by Qwen2.5-Coder-7B & NuminaMath-TIR

🚀 The Multi-Stage Pipeline

1. TIR (Thought-Intermediate-Reasoning)

2. The Resilient Engine (FastAPI Layer)

📸 Interactive Visual Samples

💻 System Configuration

🛠️ Quick Start

1. Installation

Model tree for Khurram123/SigmaMath-Visual-Core

Dataset used to train Khurram123/SigmaMath-Visual-Core