---
license: mit
language:
- en
base_model:
- AGofficial/AgGPT-14
---
<img src="banner.png" alt="AgGPT Banner" width="600"/>

# AgGPT-16 

An very light language model that can be scaled and improved easily. Built with advanced attention mechanisms, context awareness, and quality control features to deliver coherent and contextually relevant responses.

## Note

The AgGPT-16 model, despite its name, does not represent the most advanced iteration in the AgGPT series. Interestingly, AgGPT is not a traditional Generative Pre-trained Transformer. Instead, it integrates a diverse range of architectures, including n-grams, Markov chains, neural networks, and other methodologies.

Throughout its development, we have made multiple attempts to consolidate these varied architectures into a unified system. This endeavour was particularly evident in AgGPT-14. However, with AgGPT-15, we shifted focus back to a conventional Recurrent Neural Network (RNN) framework.

In AgGPT-16, we introduced a new .feather save system alongside an innovative n-gram approach. Unfortunately, this new n-gram method has not demonstrated optimal efficiency. Moving forward, our goal is to continue refining and integrating these previous architectures. Through this process, we aim to develop a fully functional and exceptionally powerful model within the AgGPT series

## Quick Start

### Basic Usage
```python
from AgGPT16 import ask

response = ask("Hello, how are you today?")
print(response)
```


## 🔧 Configuration Options

```python
ai = AgGPT16(
    model_file='custom_model.feather',  # Model save location
    max_n=5,                           # Maximum n-gram size
    output_length=150                  # Max response length
)
```

## 📊 Training Data Format

The model expects conversation data in this format:
```
user: [user message]
ai: [ai response] <|endoftext|>
```

## 🚫 Limitations

- Training time scales with corpus size
- Memory usage increases with vocabulary size
- Response quality depends on training data quality
- No external knowledge beyond training corpus

## 🤝 Contributing

This is an educational/research project. Feel free to experiment and improve upon the architecture!

## 📝 License

Open source - feel free to use and modify.