Instructions to use Khurram123/Qwen-GeoGebra-Coder-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Khurram123/Qwen-GeoGebra-Coder-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Khurram123/Qwen-GeoGebra-Coder-7B",
	filename="math_viz_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/Qwen-GeoGebra-Coder-7B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Khurram123/Qwen-GeoGebra-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Khurram123/Qwen-GeoGebra-Coder-7B: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/Qwen-GeoGebra-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Khurram123/Qwen-GeoGebra-Coder-7B: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/Qwen-GeoGebra-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Khurram123/Qwen-GeoGebra-Coder-7B: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/Qwen-GeoGebra-Coder-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Khurram123/Qwen-GeoGebra-Coder-7B:Q4_K_M

Use Docker

docker model run hf.co/Khurram123/Qwen-GeoGebra-Coder-7B:Q4_K_M

LM Studio
Jan
Ollama
How to use Khurram123/Qwen-GeoGebra-Coder-7B with Ollama:
```
ollama run hf.co/Khurram123/Qwen-GeoGebra-Coder-7B:Q4_K_M
```

Unsloth Studio new

How to use Khurram123/Qwen-GeoGebra-Coder-7B 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/Qwen-GeoGebra-Coder-7B 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/Qwen-GeoGebra-Coder-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Khurram123/Qwen-GeoGebra-Coder-7B to start chatting

Pi new

How to use Khurram123/Qwen-GeoGebra-Coder-7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Khurram123/Qwen-GeoGebra-Coder-7B: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/Qwen-GeoGebra-Coder-7B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Khurram123/Qwen-GeoGebra-Coder-7B 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/Qwen-GeoGebra-Coder-7B: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/Qwen-GeoGebra-Coder-7B:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Khurram123/Qwen-GeoGebra-Coder-7B with Docker Model Runner:
```
docker model run hf.co/Khurram123/Qwen-GeoGebra-Coder-7B:Q4_K_M
```

Lemonade

How to use Khurram123/Qwen-GeoGebra-Coder-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Khurram123/Qwen-GeoGebra-Coder-7B:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-GeoGebra-Coder-7B-Q4_K_M

List all available models

lemonade list

Qwen-GeoGebra-Coder-7B

Developed by: Khurram Pervez, Assistant Professor of Mathematics

This repository contains a specialized AI pipeline designed to transform natural language mathematical descriptions into real-time GeoGebra 3D visualizations. By combining a fine-tuned Qwen-Coder backbone with a custom FastAPI processing layer, the system bridges the gap between abstract reasoning and geometric plotting.

🚀 How It Works

The system operates via a three-stage pipeline designed for mathematical accuracy:

1. Chain-of-Thought (CoT) Reasoning

When a user submits a prompt (e.g., "Create a cylinder with radius 3 and height 10"), the model first enters a <thought> state. It calculates the spatial coordinates, base-point, and top-point vectors required by GeoGebra's engine before generating any code.

2. Smart Command Extraction (The FastAPI Layer)

LLMs often output coordinates in various styles (e.g., [<0,0,0>] or [3,0,0]). GeoGebra is strict about its syntax: Cylinder(Point, Point, Radius).

Our custom clean_and_format_ggb function acts as a mathematical transpiler:

Standardization: Converts angle brackets < > and square brackets [ ] into standard parentheses ( ).
Coordinate Mapping: Identifies base/top points and extracts the scalar radius from the spatial reasoning.
Validation: Ensures the final string is a valid GeoGebra command like Cylinder((0,0,0), (0,10,0), 3.0).

3. Real-Time Rendering

The app.html interface uses the GeoGebra Discovery/Classic API to inject the generated commands into a live 3D canvas, allowing for immediate visual verification of mathematical concepts.

📸 Interface Example

GeoGebra AI Research Interface

🛠️ Installation & Setup

Prerequisites

Hardware: NVIDIA RTX 4060 Ti (16GB VRAM recommended).
Environment: Ubuntu 22.04+, Python 3.10+, llama-cpp-python.

Local Deployment

To run this research interface on your local machine:

Download Files: Download server.py, app.html, and math_viz_Q4_K_M.gguf into one folder.

Install Requirements:

pip install fastapi uvicorn llama-cpp-python

Downloads last month: 6

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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