KerdosAI - Universal LLM Training Agent

Model Card

Model Description

KerdosAI is a universal LLM training agent designed to streamline the process of training and deploying large language models. It provides a comprehensive framework for data processing, model training, and deployment management.

Architecture Overview

graph TD
    A[Input Data] --> B[DataProcessor]
    B --> C[Data Validation]
    C --> D[Data Preprocessing]
    D --> E[Model Training]
    E --> F[Model Evaluation]
    F --> G[Model Deployment]
    
    H[Agent] --> B
    H --> E
    H --> G
    
    I[Configuration] --> H
    J[Monitoring] --> H

Component Interaction

sequenceDiagram
    participant User
    participant Agent
    participant DataProcessor
    participant Trainer
    participant Deployer
    
    User->>Agent: Initialize Training
    Agent->>DataProcessor: Process Data
    DataProcessor-->>Agent: Processed Data
    Agent->>Trainer: Train Model
    Trainer-->>Agent: Trained Model
    Agent->>Deployer: Deploy Model
    Deployer-->>Agent: Deployment Status
    Agent-->>User: Training Complete

Model Details

• Model Type: Universal LLM Training Agent
• Version: 0.1.0
• License: MIT
• Author: KerdosAI Team
• Contact: [email protected]
• Website: https://kerdos.in

System Architecture

graph LR
    subgraph Frontend
        A[CLI Interface]
        B[Web Dashboard]
    end
    
    subgraph Backend
        C[FastAPI Server]
        D[Training Service]
        E[Deployment Service]
    end
    
    subgraph Storage
        F[Model Registry]
        G[Data Storage]
        H[Logs & Metrics]
    end
    
    A --> C
    B --> C
    C --> D
    C --> E
    D --> F
    D --> G
    D --> H
    E --> F
    E --> H

Intended Use

This model is intended for:

• Training and fine-tuning large language models
• Processing and preparing training datasets
• Managing model deployment and serving
• Streamlining the ML workflow from data to deployment

Training Data

The model is designed to work with various types of training data:

• Text corpora
• Structured datasets
• Custom domain-specific data

Data Processing Pipeline

graph LR
    A[Raw Data] --> B[Data Loading]
    B --> C[Data Cleaning]
    C --> D[Feature Extraction]
    D --> E[Data Validation]
    E --> F[Processed Data]
    
    G[Quality Checks] --> C
    H[Schema Validation] --> E

Performance

The model's performance varies based on:

• Input data quality and size
• Training configuration
• Hardware resources
• Model architecture

Training Workflow

graph TD
    A[Start Training] --> B[Load Configuration]
    B --> C[Initialize Model]
    C --> D[Training Loop]
    D --> E[Validation]
    E --> F{Check Metrics}
    F -->|Not Satisfactory| D
    F -->|Satisfactory| G[Save Model]
    G --> H[End Training]
    
    I[Logging] --> D
    J[Checkpointing] --> D

Limitations

• Requires significant computational resources
• Training time depends on dataset size and complexity
• May require fine-tuning for specific use cases

Technical Details

• Framework: PyTorch
• Dependencies: See requirements.txt
• Python Version: >=3.8

Usage Example

from kerdosai import Agent, DataProcessor, Trainer, Deployer

# Initialize components
agent = Agent()
processor = DataProcessor()
trainer = Trainer()
deployer = Deployer()

# Process data
processed_data = processor.process_data(raw_data)

# Train model
model = trainer.train(processed_data)

# Deploy model
deployer.deploy(model)

Installation

pip install kerdosai

API Reference

Detailed API documentation is available at https://kerdos.in/docs.

Contributing

We welcome contributions! Please see our contributing guidelines for details.

Citation

If you use KerdosAI in your research, please cite:

@software{kerdosai2024,
  title = {KerdosAI: Universal LLM Training Agent},
  author = {KerdosAI Team},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/bhaskarvilles/kerdosai}
}

License

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