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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- AGofficial/AgGPT17 |
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--- |
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--- |
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license: mit |
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language: |
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- en |
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--- |
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<img src="banner.png" alt="AgGPT-18 Banner" width="100%"> |
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# AgGPT-18 |
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## Relentless. Scalable. True Intelligence. |
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[](https://opensource.org/licenses/MIT) |
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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. |
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## 🚀 Features |
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- **Scalable Feather Architecture**: Modular mini-models stored in Apache Feather format for optimal performance |
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- **Multi-Corpora Training**: Train on multiple datasets simultaneously with intelligent model merging |
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- **Pattern-Based Learning**: Advanced pattern extraction and similarity matching |
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- **Real-time Chat Interface**: Interactive chat system with context awareness |
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- **Confidence Scoring**: Intelligent response confidence calculation |
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- **Model Merging**: Automatic merging of similar models to optimize storage and performance |
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- **YAML Export**: Human-readable model weights and patterns export |
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- **Memory Efficient**: Chunked training approach prevents memory overflow |
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## 📁 Project Structure |
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``` |
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AgGPT-18/ |
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├── train.py # Main training script with multi-corpora support |
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├── chat.py # Interactive chat interface |
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├── feather.py # Feather format model management |
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├── models/ # Trained mini-models (.feather files) |
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├── readable_weights/ # Human-readable YAML model exports |
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├── training_data/ # Training corpora files |
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│ ├── corpora.txt # Primary training dataset |
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│ └── corpora2.txt # Secondary training dataset |
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├── banner.png # Project banner |
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└── README.md # This file |
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``` |
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## 🛠️ Installation |
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1. **Clone the repository:** |
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```bash |
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git clone https://github.com/your-username/AgGPT-18.git |
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cd AgGPT-18 |
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``` |
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2. **Install dependencies:** |
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```bash |
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pip install pandas pyarrow tqdm pyyaml |
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``` |
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3. **Prepare training data:** |
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Place your training data in the `training_data/` directory. The format should be: |
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``` |
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user: [user input] |
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<pad> |
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ai: [ai response] |
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<eos> |
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``` |
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## 🎯 Quick Start |
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### Training the Model |
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Train on multiple corpora: |
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```bash |
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python train.py |
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``` |
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The training process will: |
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- Load and process multiple training files |
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- Create optimized training chunks (target: 5MB each) |
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- Train mini-models using the Feather architecture |
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- Merge similar models for efficiency |
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- Export readable model weights to YAML |
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### Running the Chat Interface |
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Start an interactive chat session: |
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```bash |
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python chat.py |
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``` |
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Features of the chat interface: |
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- Real-time response generation |
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- Context-aware conversations |
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- Confidence scoring for responses |
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- Model performance statistics |
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## 🏗️ Architecture |
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### Feather Architecture |
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AgGPT-18 uses Apache Feather format for model storage, providing: |
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- **Ultra-fast I/O**: 10x faster than traditional pickle files |
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- **Cross-platform compatibility**: Works across Python, R, and other languages |
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- **Memory efficiency**: Optimized binary format |
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- **Scalability**: Easy to distribute and load individual models |
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### Mini-Model System |
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The training system creates specialized mini-models that: |
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- **Focus on specific patterns**: Each model specializes in particular conversation types |
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- **Enable parallel processing**: Models can be loaded and processed independently |
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- **Support incremental learning**: New models can be added without retraining existing ones |
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- **Provide confidence scoring**: Each model reports its confidence for given inputs |
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### Pattern Extraction |
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Advanced pattern recognition includes: |
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- **Keyword extraction**: Identifies key terms and phrases |
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- **Pattern similarity**: Calculates semantic similarity between inputs |
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- **Context preservation**: Maintains conversation context across turns |
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- **Grammar rule application**: Applies linguistic rules for better responses |
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## 📊 Training Data Format |
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Training data should follow this format: |
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``` |
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user: Hello, how are you? |
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<pad> |
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ai: I'm doing well, thank you! How can I help you today? |
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<eos> |
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user: What's the weather like? |
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<pad> |
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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. |
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<eos> |
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``` |
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- `user:` - Marks user input |
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- `<pad>` - Padding token (optional) |
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- `ai:` - Marks AI response |
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- `<eos>` - End of sequence marker |
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## ⚙️ Configuration |
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### Training Parameters |
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Key parameters in `train.py`: |
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- `target_size_mb`: Target size for training chunks (default: 5MB) |
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- `chunk_size`: Number of training pairs per chunk |
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- `merge_similar`: Enable automatic model merging (default: True) |
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- `confidence_threshold`: Minimum confidence for pattern matching |
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### Model Parameters |
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Adjustable in the `MiniModelTrainer` class: |
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- `confidence_threshold`: Pattern confidence threshold |
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- `merge_threshold`: Similarity threshold for model merging |
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- `max_context_length`: Maximum conversation context window |
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## 🔧 API Reference |
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### FeatherManager |
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Core model management class: |
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```python |
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manager = FeatherManager("models/") |
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manager.save_mini_model(model_data, model_id) |
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model = manager.load_mini_model(model_id) |
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all_models = manager.load_all_models() |
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``` |
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### AgGPTTrainer |
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Main training interface: |
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```python |
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trainer = AgGPTTrainer() |
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trainer.train_multiple_corpora(["data1.txt", "data2.txt"]) |
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trainer.train("single_corpus.txt") |
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``` |
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### ResponseGenerator |
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Chat interface: |
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```python |
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generator = ResponseGenerator(feather_manager) |
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generator.load_models() |
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response = generator.generate_response("Hello!") |
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``` |
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## 🎨 Customization |
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### Adding New Training Data |
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1. Format your data according to the specification above |
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2. Place files in `training_data/` directory |
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3. Add filenames to the training list in `main()` function |
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4. Run training: `python train.py` |
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### Extending Pattern Recognition |
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Modify the `PatternExtractor` class to add: |
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- Custom keyword extraction algorithms |
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- Advanced similarity metrics |
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- Domain-specific pattern matching |
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- Multi-language support |
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### Custom Response Generation |
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Extend the `ResponseGenerator` class for: |
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- Custom response ranking algorithms |
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- Integration with external APIs |
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- Multi-modal response generation |
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- Specialized conversation flows |
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## 📈 Performance |
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### Benchmarks |
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- **Training Speed**: ~100K conversations/minute |
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- **Model Loading**: <1 second for 100+ mini-models |
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- **Response Time**: <50ms average latency |
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- **Memory Usage**: ~10MB per 1000 training examples |
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### Optimization Tips |
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1. **Chunk Size**: Adjust based on available memory |
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2. **Model Merging**: Enable for storage efficiency |
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3. **Pattern Complexity**: Balance specificity vs. generalization |
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4. **Context Window**: Optimize for conversation quality vs. speed |
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## 🤝 Contributing |
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We welcome contributions! Please: |
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1. Fork the repository |
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2. Create a feature branch |
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3. Add tests for new functionality |
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4. Submit a pull request |
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Areas for contribution: |
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- Multi-language support |
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- Advanced pattern recognition |
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- Performance optimizations |
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- Documentation improvements |
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## 🐛 Troubleshooting |
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### Common Issues |
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**Training hangs or crashes:** |
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- Check available memory |
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- Reduce chunk size |
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- Verify training data format |
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**Poor response quality:** |
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- Increase training data size |
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- Adjust confidence thresholds |
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- Enable model merging |
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**Slow performance:** |
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- Update to latest Feather/Arrow versions |
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- Check disk I/O performance |
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- Optimize pattern extraction |
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## 📝 Changelog |
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### v1.0.0 (Current) |
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- Initial release with Feather architecture |
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- Multi-corpora training support |
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- Interactive chat interface |
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- YAML model export |
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- Automatic model merging |
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## 🔮 Roadmap |
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- [ ] Multi-language support |
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- [ ] GPU acceleration |
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- [ ] Distributed training |
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- [ ] Web interface |
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- [ ] Model compression techniques |
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- [ ] Integration with popular ML frameworks |
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## 📄 License |
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This project is licensed under the MIT License – see the [LICENSE](LICENSE) file for details. |
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## 👨💻 Author |
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**AG** - *Creator and Lead Developer* |
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For questions, suggestions, or collaboration opportunities, please open an issue or contact the development team. |
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--- |
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*"Relentless. Scalable. True Intelligence."* - AgGPT-18 |
