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---
title: GPU Poor LLM Arena
emoji: ๐
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.1.0
app_file: app.py
pinned: false
license: mit
short_description: 'Compact LLM Battle Arena: Frugal AI Face-Off!'
---
# ๐ GPU-Poor LLM Gladiator Arena ๐
Welcome to the GPU-Poor LLM Gladiator Arena, where frugal meets fabulous in the world of AI! This project pits compact language models (maxing out at 9B parameters) against each other in a battle of wits and words.
## ๐ค Starting from "Why?"
In the recent months, We've seen a lot of these "Tiny" models released, and some of them are really impressive.
- **Gradio Exploration**: This project serves me as a playground for experimenting with Gradio app development, I am learning how to create interactive AI interfaces with it.
- **Tiny Model Evaluation**: I wanted to develop a personal (and now public) stats system for evaluating tiny language models. It's not too serious, but it provides valuable insights into the capabilities of these compact powerhouses.
- **Accessibility**: Built on Ollama, this arena allows pretty much anyone to experiment with these models themselves. No need for expensive GPUs or cloud services!
- **Pure Fun**: At its core, this project is about having fun with AI. It's a lighthearted way to explore and compare different models. So, haters, feel free to chill โ we're just here for a good time!
## ๐ Features
- **Battle Arena**: Pit two mystery models against each other and decide which pint-sized powerhouse reigns supreme.
- **Leaderboard**: Track the performance of different models over time.
- **Performance Chart**: Visualize model performance with interactive charts.
- **Privacy-Focused**: Uses local Ollama API, avoiding pricey commercial APIs and keeping data close to home.
- **Customizable**: Easy to add new models and prompts.
## ๐ Getting Started
### Prerequisites
- Python 3.7+
- Gradio
- Plotly
- Ollama (running locally)
### Installation
1. Clone the repository:
```
git clone https://github.com/yourusername/gpu-poor-llm-gladiator-arena.git
cd gpu-poor-llm-gladiator-arena
```
2. Install the required packages:
```
pip install gradio plotly requests
```
3. Ensure Ollama is running locally or via a remote server.
4. Run the application:
```
python app.py
```
## ๐ฎ How to Use
1. Open the application in your web browser (typically at `http://localhost:7860`).
2. In the "Battle Arena" tab:
- Enter a prompt or use the random prompt generator (๐ฒ button).
- Click "Generate Responses" to see outputs from two random models.
- Vote for the better response.
3. Check the "Leaderboard" tab to see overall model performance.
4. View the "Performance Chart" tab for a visual representation of model wins and losses.
## ๐ Configuration
You can customize the arena by modifying the `arena_config.py` file:
- Add or remove models from the `APPROVED_MODELS` list.
- Adjust the `API_URL` and `API_KEY` if needed.
- Customize `example_prompts` for more variety in random prompts.
## ๐ Leaderboard
The leaderboard data is stored in `leaderboard.json`. This file is automatically updated after each battle.
## ๐ค Models
The arena currently supports various compact models, including:
- LLaMA 3.2 (1B and 3B versions)
- LLaMA 3.1 (8B version)
- Gemma 2 (2B and 9B versions)
- Qwen 2.5 (0.5B, 1.5B, 3B, and 7B versions)
- Mistral 0.3 (7B version)
- Phi 3.5 (3.8B version)
- Hermes 3 (8B version)
- Aya 23 (8B version)
## ๐ค Contributing
Contributions are welcome! Feel free to suggest a model, which is supported by Ollama. Some results are already quite surprising.
## ๐ License
This project is open-source and available under the MIT License
## ๐ Acknowledgements
- Thanks to the Ollama team for providing that amazing tool.
- Shoutout to all the AI researchers and compact language models teams, making this frugal AI arena possible!
Enjoy the battles in the GPU-Poor LLM Gladiator Arena! May the best compact model win! ๐ |