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": "Khurram123/SigmaMath-Visual-Core:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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-server -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

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

SigmaMath-Visual-Core / README.md

Khurram123

Update README.md

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	---
	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