kerdosai 0.1.0

Universal LLM Training Agent

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

pip install kerdosai

Quick Start

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.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support, please email [e-mail] or open an issue on our GitHub repository.