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

Khurram123 commited on Apr 13

Commit

ef1e0f6

verified ·

1 Parent(s): bfc0dc3

Update README.md

Browse files

Files changed (1) hide show

README.md +17 -9

README.md CHANGED Viewed

@@ -6,20 +6,22 @@ datasets:
 - AI-MO/NuminaMath-TIR
 tags:
 - mathematics
-- interactive
 - 3d-visualization
-- science
 - reasoning
 - uvicorn
 - fastapi
 ---
 <p align="center">
-  <h1 align="center">ΣMath — Visual Computation Engine v2.0</h1>
-  <p align="center"><strong>Powered by Qwen-Math & NuminaMath-TIR</strong></p>
 </p>
-## 🔬 Project Overview
 **Developed by: Khurram Pervez, Assistant Professor of Mathematics**
@@ -41,9 +43,14 @@ To ensure stability during research, the system includes a proprietary processin
 * **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**.
-### 3. Dynamic Hybrid Rendering
-* **Plotly 3D:** Used for interactive rotation, zooming, and time-series animations (e.g., Lorenz Attractors).
-* **mpld3 (2D):** Used for static or high-precision 2D mathematical function plots with browser-based interactivity.
 ## 💻 System Configuration
@@ -51,12 +58,13 @@ To ensure stability during research, the system includes a proprietary processin
 | :--- | :--- |
 | **Compute Engine** | NVIDIA GeForce RTX 4060 Ti (16GB VRAM) |
 | **Model Format** | GGUF (Quantized Q4_K_M) |
 | **OS** | Ubuntu 22.04 LTS (Optimized for `Agg` Backend) |
 | **Frameworks** | FastAPI, Llama-cpp-python, Plotly, mpld3 |
 ## 🛠️ Quick Start
-### 1. Clone & Setup
 ```bash
 # Clone this repository
 git clone [https://huggingface.co/Khurram123/SigmaMath-Visual-Core](https://huggingface.co/Khurram123/SigmaMath-Visual-Core)

 - 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 Qwen-Math & NuminaMath-TIR**
 **Developed by: Khurram Pervez, Assistant Professor of Mathematics**
 * **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%"/> |
+| **A fully interactive 3D torus rendered via Plotly, following a complex parametric prompt.** | **The professional-grade, dark-mode research dashboard showing the synthesis of neural logic.** | **The Resilient Engine silently intercepting a colorscale error and rerendering without user input.** |
 ## 💻 System Configuration
 | :--- | :--- |
 | **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)