Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
-title: Gpu Poor Llm Arena
-emoji: 🌖
+title: GPU Poor LLM Arena
+emoji: 🏆
 colorFrom: blue
 colorTo: purple
 sdk: gradio
@@ -11,4 +11,106 @@ license: mit
 short_description: 'Compact LLM Battle Arena: Frugal AI Face-Off!'
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🏆 GPU-Poor LLM Gladiator Arena 🏆
+
+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.
+
+## 🤔 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 – we're just here for a good time!
+
+
+## 🌟 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.
+
+## 🚀 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
+   ```
+
+## 🎮 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 (🎲 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.
+
+## 🛠 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.
+
+## 📊 Leaderboard
+
+The leaderboard data is stored in `leaderboard.json`. This file is automatically updated after each battle.
+
+## 🤖 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)
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to suggest a model, which is supported by Ollama. Some results are already quite surprising.
+
+## 📜 License
+
+This project is open-source and available under the MIT License
+
+## 🙏 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! 🏆