Instructions to use SuNavar/Pygenesis-Unity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SuNavar/Pygenesis-Unity with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SuNavar/Pygenesis-Unity",
	filename="pygenesis-unity-q4km.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SuNavar/Pygenesis-Unity 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 SuNavar/Pygenesis-Unity
# Run inference directly in the terminal:
llama cli -hf SuNavar/Pygenesis-Unity

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SuNavar/Pygenesis-Unity
# Run inference directly in the terminal:
llama cli -hf SuNavar/Pygenesis-Unity

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 SuNavar/Pygenesis-Unity
# Run inference directly in the terminal:
./llama-cli -hf SuNavar/Pygenesis-Unity

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 SuNavar/Pygenesis-Unity
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SuNavar/Pygenesis-Unity

Use Docker

docker model run hf.co/SuNavar/Pygenesis-Unity

LM Studio
Jan
Ollama
How to use SuNavar/Pygenesis-Unity with Ollama:
```
ollama run hf.co/SuNavar/Pygenesis-Unity
```

Unsloth Studio

How to use SuNavar/Pygenesis-Unity 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 SuNavar/Pygenesis-Unity 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 SuNavar/Pygenesis-Unity to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SuNavar/Pygenesis-Unity to start chatting

How to use SuNavar/Pygenesis-Unity with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SuNavar/Pygenesis-Unity

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": "SuNavar/Pygenesis-Unity"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SuNavar/Pygenesis-Unity with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SuNavar/Pygenesis-Unity

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 SuNavar/Pygenesis-Unity

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use SuNavar/Pygenesis-Unity with Docker Model Runner:
```
docker model run hf.co/SuNavar/Pygenesis-Unity
```

Lemonade

How to use SuNavar/Pygenesis-Unity with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SuNavar/Pygenesis-Unity

Run and chat with the model

lemonade run user.Pygenesis-Unity-{{QUANT_TAG}}

List all available models

lemonade list

Pygenesis-Unity / README.md

SuNavar

Update README.md

37c333c verified 5 days ago

preview code

Raw

History Blame Contribute Delete

1.91 kB

metadata

license: gfdl
language:
  - es
  - en
base_model:
  - Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - gguf
  - unity
  - csharp
  - code

Pygenesis Unity GGUF (Qwen 2.5 Coder Fine-Tuned)

Pygenesis Unity is a specialized, fine-tuned LLM based on the Qwen 2.5 Coder architecture, optimized for Unity game development and advanced C# scripting. This repository contains the model weights in GGUF format, making it perfect for efficient local inference.

The model was fine-tuned using a curated dataset of nearly 1,000 high-quality, domain-specific instruction-response pairs sourced from official Unity documentation, advanced C# manuals, and high-tier synthetic data.

Model Description

Developed by: Pygenesis Association
Base Model: Qwen 2.5 Coder
Format: GGUF (Optimized for local deployment)
Specialization: Unity Engine & C# Language

Training Details

The training pipeline focused heavily on structure and logic:

Full coverage of Unity Manual best practices, Monobehaviours, Scriptable Objects, and performance optimization.
Advanced C# scripting patterns applied to game design.
Instructional data distillation using frontier models to maximize code accuracy and deep reasoning capabilities.

Intended Use

Pygenesis Unity is tailored for indie developers and technical leads who want a privacy-first, offline assistant to:

Generate clean, optimized C# scripts for Unity loops and systems.
Debug engine-specific code and refactor legacy scripts.
Implement performance-oriented architecture (such as object pooling, memory management, or basic DOTS structures).

How to Use

Since this model is provided in GGUF format, you can run it locally using various inference engines.

Example using Llama.cpp CLI:

./llama-cli -m pygenesis-unity-qwen2.5-coder.gguf -p "Write a highly optimized C# script for an object pooling system in Unity." -n 512