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

11efb81

verified ·

1 Parent(s): b79ab98

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -6

README.md CHANGED Viewed

@@ -1,7 +1,7 @@
 ---
 license: mit
 library_name: gguf
-base_model: Qwen/Qwen2.5-Math-7B
 datasets:
 - AI-MO/NuminaMath-TIR
 tags:
@@ -21,21 +21,21 @@ tags:
 # ΣMath — Visual Computation Engine v2.0
-### **Powered by Qwen-Math & 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 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 doesn't just "guess" a plot. It 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).
 ### 2. The Resilient Engine (FastAPI Layer)
 To ensure stability during research, the system includes a proprietary processing layer:
@@ -50,7 +50,6 @@ Here are examples of advanced parametric surfaces generated in real-time by **Σ
 | 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

 ---
 license: mit
 library_name: gguf
+base_model: Qwen/Qwen2.5-Coder-7B
 datasets:
 - AI-MO/NuminaMath-TIR
 tags:
 # 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:
 | 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