Metadata-Version: 2.4
Name: kerdosai
Version: 0.2.0
Summary: Universal LLM Training & RAG Agent for HuggingFace
Author: KerdosAI Team
Author-email: contact@kerdosai.com
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2.0.0
Requires-Dist: transformers>=4.30.0
Requires-Dist: datasets>=2.12.0
Requires-Dist: accelerate>=0.20.0
Requires-Dist: sentencepiece>=0.1.99
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---
license: apache-2.0
---

# KerdosAI - Advanced Universal LLM Training Agent

## Model Description

KerdosAI is a sophisticated AI agent framework designed to revolutionize how organizations train, customize, and deploy Large Language Models (LLMs). It provides an enterprise-grade solution that combines advanced training techniques, robust data processing, and flexible deployment options while ensuring data privacy and security.

### Key Features

- **Universal LLM Integration**: Seamlessly integrates with any LLM architecture (GPT, BERT, T5, etc.)
- **Advanced Training Pipeline**: 
  - Multi-stage training with curriculum learning
  - Automatic hyperparameter optimization
  - Distributed training support
  - Gradient checkpointing and mixed precision training
- **Enterprise-Grade Security**:
  - End-to-end encryption
  - Role-based access control
  - Audit logging
  - Data anonymization
- **Intelligent Data Processing**:
  - Automatic data quality assessment
  - Smart data cleaning and normalization
  - Multi-language support
  - Domain-specific preprocessing
- **Scalable Architecture**:
  - Horizontal and vertical scaling
  - Load balancing
  - Auto-scaling capabilities
  - Resource optimization

## Real-World Applications

### 1. Healthcare
```mermaid
graph LR
    A[Medical Records] --> B[Data Anonymization]
    B --> C[Domain Adaptation]
    C --> D[Clinical Assistant]
    D --> E[Patient Care]
    D --> F[Medical Research]
```

- **Clinical Documentation**: Automate medical report generation
- **Patient Care**: Create personalized care plans
- **Research**: Analyze medical literature and clinical trials
- **Compliance**: Ensure HIPAA compliance and data privacy

### 2. Financial Services
```mermaid
graph LR
    A[Financial Data] --> B[Risk Analysis]
    B --> C[Compliance Check]
    C --> D[Customer Service]
    D --> E[Fraud Detection]
    D --> F[Investment Advice]
```

- **Risk Assessment**: Analyze market trends and risks
- **Customer Support**: Provide personalized financial advice
- **Compliance**: Ensure regulatory compliance
- **Fraud Detection**: Identify suspicious transactions

### 3. Legal Services
```mermaid
graph LR
    A[Legal Documents] --> B[Document Analysis]
    B --> C[Case Research]
    C --> D[Legal Assistant]
    D --> E[Contract Review]
    D --> F[Case Prediction]
```

- **Document Review**: Automate legal document analysis
- **Case Research**: Summarize legal precedents
- **Contract Analysis**: Review and analyze contracts
- **Case Outcome Prediction**: Predict case outcomes

## Technical Architecture

### Core Components

```mermaid
graph TD
    A[Input Data] --> B[Data Processor]
    B --> C[Training Pipeline]
    C --> D[Model Adaptation]
    D --> E[Deployment Manager]
    
    subgraph Data Processing
        B --> B1[Data Validation]
        B --> B2[Text Cleaning]
        B --> B3[Tokenization]
        B --> B4[Quality Assessment]
        B --> B5[Domain Adaptation]
    end
    
    subgraph Training
        C --> C1[Model Loading]
        C --> C2[Curriculum Learning]
        C --> C3[Hyperparameter Optimization]
        C --> C4[Distributed Training]
        C --> C5[Evaluation]
    end
    
    subgraph Deployment
        E --> E1[REST API]
        E --> E2[Docker]
        E --> E3[Kubernetes]
        E --> E4[Monitoring]
        E --> E5[Auto-scaling]
    end
```

### Advanced Training Pipeline

```mermaid
sequenceDiagram
    participant User
    participant KerdosAgent
    participant DataProcessor
    participant Optimizer
    participant Trainer
    participant Evaluator
    participant Deployer
    
    User->>KerdosAgent: Initialize with base model
    KerdosAgent->>DataProcessor: Process training data
    DataProcessor-->>KerdosAgent: Validated dataset
    KerdosAgent->>Optimizer: Optimize hyperparameters
    Optimizer-->>KerdosAgent: Optimal parameters
    KerdosAgent->>Trainer: Train with curriculum
    Trainer->>Evaluator: Evaluate performance
    Evaluator-->>Trainer: Evaluation metrics
    Trainer-->>KerdosAgent: Training results
    KerdosAgent->>Deployer: Deploy model
    Deployer-->>User: Deployment status
```

### Requirements

- Python 3.8+
- PyTorch 2.0+
- Transformers 4.30+
- CUDA-compatible GPU (recommended for training)
- 16GB+ RAM (32GB recommended)
- 100GB+ storage for large datasets
- Docker (for containerized deployment)
- Kubernetes (for orchestration)

## Advanced Features

### 1. Curriculum Learning
- Progressive training from simple to complex tasks
- Automatic difficulty assessment
- Dynamic curriculum adjustment
- Multi-task learning support

### 2. Hyperparameter Optimization
- Bayesian optimization
- Grid and random search
- Early stopping with patience
- Learning rate scheduling

### 3. Distributed Training
- Data parallel training
- Model parallel training
- Gradient synchronization
- Checkpoint management

### 4. Advanced Deployment
- Blue-green deployment
- Canary releases
- A/B testing
- Performance monitoring
- Auto-scaling

## Installation

```bash
# Basic installation
pip install kerdosai

# Installation with all optional dependencies
pip install "kerdosai[all]"

# Installation for GPU support
pip install "kerdosai[gpu]"
```

## Advanced Usage

```python
from kerdosai import KerdosAgent, TrainingConfig, DeploymentConfig

# Initialize with advanced configuration
config = TrainingConfig(
    curriculum_learning=True,
    hyperparameter_optimization=True,
    distributed_training=True,
    mixed_precision=True
)

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

# Train with advanced features
agent.train(
    epochs=5,
    batch_size=8,
    learning_rate=2e-5,
    curriculum_steps=10,
    optimization_rounds=20
)

# Deploy with monitoring
deploy_config = DeploymentConfig(
    monitoring=True,
    auto_scaling=True,
    blue_green=True
)

agent.deploy(
    deployment_type="kubernetes",
    config=deploy_config
)
```

## Training Process

### 1. Data Preparation
- Data quality assessment
- Automatic cleaning and normalization
- Domain-specific preprocessing
- Multi-language support
- Data augmentation

### 2. Model Training
- Curriculum-based learning
- Hyperparameter optimization
- Distributed training
- Mixed precision training
- Gradient checkpointing

### 3. Evaluation
- Multiple metrics tracking
- Cross-validation
- Domain-specific evaluation
- Performance benchmarking
- Bias detection

## Deployment Options

### 1. REST API
- FastAPI backend
- OpenAPI documentation
- Rate limiting
- Authentication
- Request validation

### 2. Docker
- Multi-stage builds
- Optimized images
- Health checks
- Resource limits
- Volume management

### 3. Kubernetes
- Horizontal pod autoscaling
- Resource quotas
- Network policies
- Service mesh integration
- Monitoring and logging

## Performance Optimization

### Training Performance
- Automatic batch size optimization
- Gradient accumulation
- Memory optimization
- Distributed training
- Mixed precision training

### Inference Performance
- Model quantization
- Batch inference
- Caching
- Load balancing
- Auto-scaling

## Security Features

### Data Security
- End-to-end encryption
- Data anonymization
- Access control
- Audit logging
- Compliance reporting

### Model Security
- Model watermarking
- Adversarial training
- Input validation
- Output sanitization
- Rate limiting

## Monitoring and Maintenance

### Monitoring
- Performance metrics
- Resource usage
- Error tracking
- User analytics
- Cost monitoring

### Maintenance
- Automatic updates
- Backup and recovery
- Version control
- Rollback capability
- Health checks

## Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.

## License

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

## Citation

If you use KerdosAI in your research, please cite:

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

## Contact

For questions and support, please open an issue in the GitHub repository or contact the maintainers.
