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@@ -4,16 +4,16 @@ emoji: πŸ†
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  colorFrom: blue
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  colorTo: purple
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  sdk: gradio
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- sdk_version: 5.8.0
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  app_file: app.py
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- pinned: false
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  license: mit
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  short_description: 'Compact LLM Battle Arena: Frugal AI Face-Off!'
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  ---
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  # πŸ† GPU-Poor LLM Gladiator Arena πŸ†
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- 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.
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  ## πŸ€” Starting from "Why?"
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@@ -31,10 +31,11 @@ In the recent months, we've seen a lot of these "Tiny" models released, and some
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  ## 🌟 Features
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  - **Battle Arena**: Pit two mystery models against each other and decide which pint-sized powerhouse reigns supreme.
 
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  - **Leaderboard**: Track the performance of different models over time using an improved scoring system.
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  - **Performance Chart**: Visualize model performance with interactive charts.
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  - **Privacy-Focused**: Uses local Ollama API, avoiding pricey commercial APIs and keeping data close to home.
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- - **Customizable**: Easy to add new models and prompts.
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  ## πŸš€ Getting Started
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@@ -43,7 +44,9 @@ In the recent months, we've seen a lot of these "Tiny" models released, and some
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  - Python 3.7+
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  - Gradio
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  - Plotly
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- - Ollama (running locally)
 
 
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  ### Installation
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@@ -55,7 +58,7 @@ In the recent months, we've seen a lot of these "Tiny" models released, and some
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  2. Install the required packages:
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  ```
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- pip install gradio plotly requests
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  ```
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  3. Ensure Ollama is running locally or via a remote server.
@@ -71,9 +74,11 @@ In the recent months, we've seen a lot of these "Tiny" models released, and some
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  2. In the "Battle Arena" tab:
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  - Enter a prompt or use the random prompt generator (🎲 button).
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  - Click "Generate Responses" to see outputs from two random models.
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- - Vote for the better response.
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  3. Check the "Leaderboard" tab to see overall model performance.
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  4. View the "Performance Chart" tab for a visual representation of model wins and losses.
 
 
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  ## πŸ›  Configuration
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@@ -137,52 +142,24 @@ In addition to the main leaderboard, we also maintain an ELO-based leaderboard:
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  ## πŸ€– Models
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- The arena currently supports the following compact models:
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-
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- - LLaMA 3.2 (1B)
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- - LLaMA 3.2 (3B)
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- - LLaMA 3.1 (8B)
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- - Gemma 2 (2B)
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- - Gemma 2 (9B)
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- - Qwen 2.5 (0.5B)
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- - Qwen 2.5 (1.5B)
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- - Qwen 2.5 (3B)
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- - Qwen 2.5 (7B)
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- - Phi 3.5 (3.8B)
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- - Mistral 0.3 (7B)
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- - Hermes 3 (8B)
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- - Aya 23 (8B)
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- - Granite 3 Dense (2B)
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- - Granite 3 Dense (8B)
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- - Granite 3 MoE (1B)
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- - Granite 3 MoE (3B)
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- - Ministral (8B)
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- - Dolphin 2.9.4 (8B)
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- - Yi v1.5 (6B)
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- - Yi v1.5 (9B)
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- - Mistral Nemo (12B)
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- - GLM4 (9B)
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- - InternLM2 v2.5 (7B)
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- - Falcon2 (11B)
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- - StableLM2 (1.6B)
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- - StableLM2 (12B)
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- - Solar (10.7B)
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- - Rombos Qwen (7B)
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- - Rombos Qwen (1.5B)
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- - Aya Expanse (8B)
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- - SmolLM2 (1.7B)
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- - TinyLLama (1.1B)
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- - Pints (1.57B)
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- - OLMoE (7B)
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- - Llama 3.2 Uncensored (3B)
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- - Llama 3.1 Hawkish (8B)
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- - Humanish Llama 3 (8B)
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- - Nemotron Mini (4B)
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- - Teuken (7B)
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- - Llama 3.1 Sauerkraut (8B)
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- - Llama 3.1 SuperNova Lite (8B)
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- - EuroLLM (9B)
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- - Intellect-1 (10B)
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  ## 🀝 Contributing
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  colorFrom: blue
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  colorTo: purple
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  sdk: gradio
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+ sdk_version: 5.9.1
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  app_file: app.py
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+ pinned: true
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  license: mit
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  short_description: 'Compact LLM Battle Arena: Frugal AI Face-Off!'
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  ---
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  # πŸ† GPU-Poor LLM Gladiator Arena πŸ†
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+ 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 14B parameters) against each other in a battle of wits and words.
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  ## πŸ€” Starting from "Why?"
19
 
 
31
  ## 🌟 Features
32
 
33
  - **Battle Arena**: Pit two mystery models against each other and decide which pint-sized powerhouse reigns supreme.
34
+ - **Dynamic Model Management**: Models list is managed remotely, allowing for easy updates without code changes.
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  - **Leaderboard**: Track the performance of different models over time using an improved scoring system.
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  - **Performance Chart**: Visualize model performance with interactive charts.
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  - **Privacy-Focused**: Uses local Ollama API, avoiding pricey commercial APIs and keeping data close to home.
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+ - **Model Suggestions**: Users can suggest new models to be added to the arena.
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  ## πŸš€ Getting Started
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  - Python 3.7+
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  - Gradio
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  - Plotly
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+ - OpenAI Python library (for API compatibility)
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+ - Nextcloud Python API
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+ - Ollama (running via OpenAI compatible API wrapper)
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  ### Installation
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  2. Install the required packages:
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  ```
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+ pip install gradio plotly openai nc_py_api
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  ```
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  3. Ensure Ollama is running locally or via a remote server.
 
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  2. In the "Battle Arena" tab:
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  - Enter a prompt or use the random prompt generator (🎲 button).
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  - Click "Generate Responses" to see outputs from two random models.
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+ - Vote for the better response or choose "Tie" to continue the battle.
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  3. Check the "Leaderboard" tab to see overall model performance.
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  4. View the "Performance Chart" tab for a visual representation of model wins and losses.
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+ 5. Check the "ELO Leaderboard" for an alternative ranking system.
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+ 6. Use the "Suggest Models" tab to propose new models for the arena.
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  ## πŸ›  Configuration
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  ## πŸ€– Models
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+ The arena supports a dynamic list of models that is updated regularly. The current list includes models from various families such as:
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+
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+ - LLaMA 3.x series (1B to 8B)
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+ - Gemma 2 (2B and 9B)
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+ - Qwen 2.5 (0.5B to 7B)
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+ - Mistral and variants
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+ - Yi models
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+ - And many more!
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+
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+ For the complete and current list of models, check the arena's leaderboard.
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+
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+ ## πŸ›  Technical Details
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+
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+ The project uses:
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+ - Nextcloud for remote storage of models list and leaderboard data
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+ - OpenAI-compatible API interface for model interactions
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+ - Background thread for periodic model list updates
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+ - ELO rating system with size-based adjustments
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ## 🀝 Contributing
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