Synthetic Data Generation Application

An application for generating high-quality synthetic datasets for various fine-tuning techniques. The application currently specializes in code generation and SQL query synthesis, with support for custom use cases.

Table of Contents

• Features
• Architecture
• Installation
• API Endpoints
• Usage Examples
• Legal Notice

Features

Data Generation

• Support for multiple fine-tuning techniques:
  • ✅ Supervised Fine-Tuning (SFT) - Currently Live
  • 🚧 PPO (Proximal Policy Optimization) - WIP
  • 🚧 ORPO (Odds Ratio Preference Optimization) - WIP
  • 🚧 DPO (Direct Preference Optimization) - WIP
  • 🚧 KTO (Kahneman-Tversky Optimisation) - WIP

Use Cases

Code Generation

Generate diverse programming question-answer pairs across multiple domains, complete with detailed explanations and working code examples. The system creates scenarios that test both theoretical understanding and practical implementation skills, producing high-quality training data for code-assistance models.

Text-to-SQL

Generate data as prompt and SQL pairs on custom data schemas which can be used to further fine-tune models for enhanced text2sql performance on OSS models.

Custom Use Cases

Flexible framework for implementing additional use cases which allows users to create their own workflow.

Model Integration

AWS Bedrock Integration

• Claude 3 Family
• Llama 3 Models
• Mistral Models

Cloudera AI Inference (CAII) Support

Evaluation

• Built-in evaluation capabilities for generated datasets
• Customizable evaluation prompts
• Scoring system with detailed justifications

Output Modes

• Preview Mode (displayed on Front-End) for prompts solution pairs <= 25
• Batch Mode via Cloudera ML Jobs (User can run this only in Cloudera environment)

Architecture

Built using:

• Backend: FastAPI
• Frontend: React
• Database: SQLite (for metadata storage)

Installation

1. Clone the repository:

   git clone <repository-url>

2. Configure environment variables:

   # AWS Bedrock credentials (in CML environment)
   export AWS_ACCESS_KEY_ID="your key"
   export AWS_SECRET_ACCESS_KEY="your secret key"
   export AWS_DEFAULT_REGION="aws region"

   Note: If using AWS Bedrock, ensure you have access to the LLM you intend to use.

3. Build the application:

   python build_client.py

4. Start the application:

   python start_application.py

API Endpoints(backend)

Data Generation

• /synthesis/generate: Generate synthetic Q&A pairs
• /synthesis/evaluate: Evaluate generated examples

Configuration

• /model/model_ID: Get available model configurations
• /use-cases: List available use cases
• /model/parameters: Get model parameter ranges
• /{use_case}/gen_prompt: Get generation prompts
• /{use_case}/eval_prompt: Get evaluation prompts

Management

• /generations/history: View generation history
• /evaluations/history: View evaluation history
• /generations/display-name: Update generation metadata
• /evaluations/display-name: Update evaluation metadata

Usage Examples

Code Generation Request

{
    "use_case": "code_generation",
    "model_id": "anthropic.claude-3-5-sonnet-20240620-v1:0",
    "num_questions": 3,
    "technique": "sft",
    "topics": ["python_basics", "data_structures"],
    "examples": [
        {
            "question": "How do you create a list in Python and add elements to it?",
            "solution": "# Example solution code..."
        }
    ],
   
    "model_params": {
        "temperature": 0.0,
        "top_p": 1.0,
        "max_tokens": 4096
    }
}

Text-to-SQL Request

{
    "use_case": "text2sql",
    "model_id": "anthropic.claude-3-5-sonnet-20240620-v1:0",
    "num_questions": 3,
    "technique": "sft",
    "topics": ["basic_queries", "joins"],
    "schema": "CREATE TABLE users (...)",
    "examples": [
        {
            "question": "How do you select all employees from the employees table?",
            "solution": "SELECT * FROM employees;"
        }
    ],
   
    "model_params": {
        "temperature": 0.0,
        "top_p": 1.0,
        "max_tokens": 4096
    }
}

Legal Notice

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