🎯 LLM Optimization Tools

A collection of tools and techniques to optimize your LLM applications - reduce costs, improve performance, and maximize efficiency.

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📚 Tools Available

🎯 Toonify Token Optimization

Reduce LLM API costs by 30-60% using TOON (Token-Oriented Object Notation) format.

What it does:

• Converts JSON data to compact TOON format
• Reduces token usage significantly
• Maintains data structure and readability
• Saves money on API calls

Key Features:

• ✅ 63.9% average token reduction vs JSON
• ✅ Up to 73.4% savings for tabular data
• ✅ Human-readable format
• ✅ Roundtrip conversion (JSON ↔ TOON)
• ✅ Schema validation support
• ✅ Interactive Streamlit app

Quick Example:

from toon import encode, decode

# Your data (247 bytes as JSON)
data = {
  "products": [
    {"id": 101, "name": "Laptop Pro", "price": 1299},
    {"id": 102, "name": "Magic Mouse", "price": 79}
  ]
}

# Convert to TOON (98 bytes - 60% reduction!)
toon_str = encode(data)
# products[2]{id,name,price}:
#   101,Laptop Pro,1299
#   102,Magic Mouse,79

# Pass to LLM with reduced cost
response = llm.complete(f"Analyze: {toon_str}")

Use Cases:

• 📊 Pass large datasets to LLMs
• 💰 Reduce API costs significantly
• 🔄 Optimize context window usage
• 📈 Improve response times

Get Started:

cd toonify_token_optimization/
pip install -r requirements.txt
python quick_test.py

📖 Full Documentation →

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💡 Why Optimize?

Cost Savings

LLM API costs are based on token count. Reducing tokens = saving money!

Example Savings (GPT-4):

• 1,000 API calls: $2.15 saved
• 100,000 API calls: $214.70 saved
• 1M API calls: $2,147.00 saved 💰

Performance

Fewer tokens = faster processing and better efficiency.

Context Window

Maximize what you can fit in your context window by using compact formats.

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🎯 Best Practices

1. Use Compact Formats for Structured Data

When passing data to LLMs, use efficient serialization:

• ✅ TOON for tabular/structured data
• ✅ CSV for simple datasets
• ❌ Avoid verbose JSON with excessive whitespace

2. Optimize Prompts

• Be concise and clear
• Remove unnecessary examples
• Use structured formats

3. Batch Processing

• Group similar requests
• Reuse context when possible
• Cache frequent responses

4. Choose the Right Model

• Use smaller models for simple tasks
• Reserve GPT-4 for complex reasoning
• Consider fine-tuned models

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📊 Comparison Table

Format	Size	Tokens	Cost (per 1M calls)	Best For
JSON (verbose)	247 B	85	$2,550	Compatibility
JSON (compact)	189 B	67	$2,010	Standard use
TOON	98 B	39	$1,170	Structured data
CSV	112 B	42	$1,260	Simple tables

Based on GPT-4 pricing ($0.03/1K input tokens)

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🚀 Future Tools (Coming Soon)

Planned Additions:

📦 Prompt Compression

Automatically compress long prompts while preserving meaning.

🗜️ Context Optimization

Smart context window management for long conversations.

📈 Token Analytics

Track and analyze token usage across your applications.

💾 Response Caching

Intelligent caching to avoid redundant API calls.

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🤝 Contributing

Have an optimization technique to share? We'd love to include it!

How to contribute: 2. Create a new folder for your tool 3. Include README, code, and examples 4. Submit a pull request

Guidelines:

• Must significantly reduce costs or improve performance
• Include benchmarks and comparisons
• Provide clear documentation
• Add usage examples

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📖 Additional Resources

Learning Resources

• LLM Token Basics
• Cost Optimization Guide
• Best Practices for Production

💬 Support

• 📧 Questions? Open an issue on GitHub
• 💡 Suggestions? We're always looking for new optimization techniques!

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📄 License

Tools in this collection may have different licenses. Check each tool's folder for specific license information.

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Save money, go faster, build better! 🚀💰