---
license: mit
language:
- en
base_model:
- AGofficial/AgGPT17
---
---  
license: mit  
language:  
- en  
---

<img src="banner.png" alt="AgGPT-18 Banner" width="100%">

# AgGPT-18

## Relentless. Scalable. True Intelligence.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

AgGPT-18 is a revolutionary AI training framework that implements a **Scalable Feather Architecture** for building efficient, modular AI models. This system breaks down large training datasets into manageable mini-models, each stored in highly optimized Feather format files for lightning-fast loading and inference.

## 🚀 Features

- **Scalable Feather Architecture**: Modular mini-models stored in Apache Feather format for optimal performance
- **Multi-Corpora Training**: Train on multiple datasets simultaneously with intelligent model merging
- **Pattern-Based Learning**: Advanced pattern extraction and similarity matching
- **Real-time Chat Interface**: Interactive chat system with context awareness
- **Confidence Scoring**: Intelligent response confidence calculation
- **Model Merging**: Automatic merging of similar models to optimize storage and performance
- **YAML Export**: Human-readable model weights and patterns export
- **Memory Efficient**: Chunked training approach prevents memory overflow

## 📁 Project Structure

```
AgGPT-18/
├── train.py              # Main training script with multi-corpora support
├── chat.py               # Interactive chat interface
├── feather.py            # Feather format model management
├── models/               # Trained mini-models (.feather files)
├── readable_weights/     # Human-readable YAML model exports
├── training_data/        # Training corpora files
│   ├── corpora.txt      # Primary training dataset
│   └── corpora2.txt     # Secondary training dataset
├── banner.png           # Project banner
└── README.md            # This file
```

## 🛠️ Installation

1. **Clone the repository:**
   ```bash
   git clone https://github.com/your-username/AgGPT-18.git
   cd AgGPT-18
   ```

2. **Install dependencies:**
   ```bash
   pip install pandas pyarrow tqdm pyyaml
   ```

3. **Prepare training data:**
   Place your training data in the `training_data/` directory. The format should be:
   ```
   user: [user input]
   <pad>
   ai: [ai response]
   <eos>
   ```

## 🎯 Quick Start

### Training the Model

Train on multiple corpora:
```bash
python train.py
```

The training process will:
- Load and process multiple training files
- Create optimized training chunks (target: 5MB each)
- Train mini-models using the Feather architecture
- Merge similar models for efficiency
- Export readable model weights to YAML

### Running the Chat Interface

Start an interactive chat session:
```bash
python chat.py
```

Features of the chat interface:
- Real-time response generation
- Context-aware conversations
- Confidence scoring for responses
- Model performance statistics

## 🏗️ Architecture

### Feather Architecture

AgGPT-18 uses Apache Feather format for model storage, providing:
- **Ultra-fast I/O**: 10x faster than traditional pickle files
- **Cross-platform compatibility**: Works across Python, R, and other languages
- **Memory efficiency**: Optimized binary format
- **Scalability**: Easy to distribute and load individual models

### Mini-Model System

The training system creates specialized mini-models that:
- **Focus on specific patterns**: Each model specializes in particular conversation types
- **Enable parallel processing**: Models can be loaded and processed independently
- **Support incremental learning**: New models can be added without retraining existing ones
- **Provide confidence scoring**: Each model reports its confidence for given inputs

### Pattern Extraction

Advanced pattern recognition includes:
- **Keyword extraction**: Identifies key terms and phrases
- **Pattern similarity**: Calculates semantic similarity between inputs
- **Context preservation**: Maintains conversation context across turns
- **Grammar rule application**: Applies linguistic rules for better responses

## 📊 Training Data Format

Training data should follow this format:

```
user: Hello, how are you?
<pad>
ai: I'm doing well, thank you! How can I help you today?
<eos>

user: What's the weather like?
<pad>
ai: I don't have access to real-time weather data, but I'd be happy to help you find weather information from a reliable source.
<eos>
```

- `user:` - Marks user input
- `<pad>` - Padding token (optional)
- `ai:` - Marks AI response
- `<eos>` - End of sequence marker

## ⚙️ Configuration

### Training Parameters

Key parameters in `train.py`:
- `target_size_mb`: Target size for training chunks (default: 5MB)
- `chunk_size`: Number of training pairs per chunk
- `merge_similar`: Enable automatic model merging (default: True)
- `confidence_threshold`: Minimum confidence for pattern matching

### Model Parameters

Adjustable in the `MiniModelTrainer` class:
- `confidence_threshold`: Pattern confidence threshold
- `merge_threshold`: Similarity threshold for model merging
- `max_context_length`: Maximum conversation context window

## 🔧 API Reference

### FeatherManager

Core model management class:

```python
manager = FeatherManager("models/")
manager.save_mini_model(model_data, model_id)
model = manager.load_mini_model(model_id)
all_models = manager.load_all_models()
```

### AgGPTTrainer

Main training interface:

```python
trainer = AgGPTTrainer()
trainer.train_multiple_corpora(["data1.txt", "data2.txt"])
trainer.train("single_corpus.txt")
```

### ResponseGenerator

Chat interface:

```python
generator = ResponseGenerator(feather_manager)
generator.load_models()
response = generator.generate_response("Hello!")
```

## 🎨 Customization

### Adding New Training Data

1. Format your data according to the specification above
2. Place files in `training_data/` directory
3. Add filenames to the training list in `main()` function
4. Run training: `python train.py`

### Extending Pattern Recognition

Modify the `PatternExtractor` class to add:
- Custom keyword extraction algorithms
- Advanced similarity metrics
- Domain-specific pattern matching
- Multi-language support

### Custom Response Generation

Extend the `ResponseGenerator` class for:
- Custom response ranking algorithms
- Integration with external APIs
- Multi-modal response generation
- Specialized conversation flows

## 📈 Performance

### Benchmarks

- **Training Speed**: ~100K conversations/minute
- **Model Loading**: <1 second for 100+ mini-models
- **Response Time**: <50ms average latency
- **Memory Usage**: ~10MB per 1000 training examples

### Optimization Tips

1. **Chunk Size**: Adjust based on available memory
2. **Model Merging**: Enable for storage efficiency
3. **Pattern Complexity**: Balance specificity vs. generalization
4. **Context Window**: Optimize for conversation quality vs. speed

## 🤝 Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Submit a pull request

Areas for contribution:
- Multi-language support
- Advanced pattern recognition
- Performance optimizations
- Documentation improvements

## 🐛 Troubleshooting

### Common Issues

**Training hangs or crashes:**
- Check available memory
- Reduce chunk size
- Verify training data format

**Poor response quality:**
- Increase training data size
- Adjust confidence thresholds
- Enable model merging

**Slow performance:**
- Update to latest Feather/Arrow versions
- Check disk I/O performance
- Optimize pattern extraction

## 📝 Changelog

### v1.0.0 (Current)
- Initial release with Feather architecture
- Multi-corpora training support
- Interactive chat interface
- YAML model export
- Automatic model merging

## 🔮 Roadmap

- [ ] Multi-language support
- [ ] GPU acceleration
- [ ] Distributed training
- [ ] Web interface
- [ ] Model compression techniques
- [ ] Integration with popular ML frameworks

## 📄 License

This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details.

## 👨‍💻 Author

**AG** - *Creator and Lead Developer*

For questions, suggestions, or collaboration opportunities, please open an issue or contact the development team.

---

*"Relentless. Scalable. True Intelligence."* - AgGPT-18