# KerdosAI - Universal LLM Training Agent

KerdosAI is a versatile AI agent designed to be seamlessly integrated with any Large Language Model (LLM). It provides a comprehensive solution for companies to train, customize, and deploy LLMs with their proprietary data while maintaining full control over their infrastructure.

## Key Features

- **Universal LLM Integration**: Compatible with any existing LLM architecture
- **Custom Training Pipeline**: Streamlined process for training with company-specific data
- **Infrastructure Agnostic**: Can be deployed in any cloud or on-premise environment
- **Data Privacy**: Built-in mechanisms to ensure data security and privacy
- **Scalable Architecture**: Designed for enterprise-scale deployments

## Installation

```bash
pip install kerdosai
```

## Quick Start

```python
from kerdosai import KerdosAgent

# Initialize the agent
agent = KerdosAgent(
    base_model="your-llm-model",
    training_data="path/to/your/data"
)

# Train the model
agent.train()

# Deploy the model
agent.deploy()
```

## Requirements

- Python 3.8+
- PyTorch 2.0+
- Transformers 4.30+
- CUDA-compatible GPU (recommended for training)

## Features

### Training
- Data preprocessing and validation
- Model adaptation
- Fine-tuning
- Evaluation and validation

### Deployment
- REST API support
- Docker container support
- Kubernetes cluster support
- Cloud platform integration (AWS, Azure, Google Cloud)

## Performance

- Training time varies based on dataset size and hardware
- Inference latency: < 100ms on standard GPU
- Memory requirements: 8GB minimum, 16GB recommended

## Limitations

- Requires significant computational resources for training
- Training time increases with dataset size
- May require fine-tuning for specific use cases

## Documentation

For detailed documentation, please visit our [documentation page](https://kerdos.in/docs).

## License

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

## Support

For support, please email support@kerdos.in or visit our [support page](https://kerdos.in/contact). 