ALMEV - GPT-OSS-20B Fine-tuned for MEV Detection

🚀 20B Parameter LLM Specialized for Maximum Extractable Value

This is the full GPT-OSS-20B model (13GB) enhanced with LoRA adapters specifically trained for MEV detection on Solana blockchain.

Model Architecture

• Base Model: GPT-OSS-20B (13GB quantized)
• Total Parameters: 20 billion + 315K MEV adapter
• Adapter Type: LoRA (Low-Rank Adaptation)
• Training Method: Multi-task learning with regularization
• Validation Accuracy: 99.3%

Training Details

• Dataset: 700,805 Solana transactions
• MEV Types Detected:
  • Arbitrage opportunities
  • Sandwich attacks
  • Liquidation events
  • Front-running patterns
• Training Hardware: Apple M4 Max (MPS)
• Optimization: AdamW with weight decay

Model Components

Component	Description	Size
Base Model	GPT-OSS-20B (quantized)	13GB
MEV Adapter	LoRA fine-tuning weights	1.2MB
Total Size	Full model	~13GB

Usage

With Ollama

# Install the model
ollama pull zpphxd/almev

# Run interactive session
ollama run zpphxd/almev

Example Prompts

"Analyze this transaction for MEV opportunities: {tx_data}"
"What profit can be extracted from this arbitrage?"
"Identify sandwich attack patterns in these transactions"

Python Integration

import ollama

client = ollama.Client()
response = client.generate(
    model='zpphxd/almev',
    prompt='Analyze MEV opportunity: compute=500000, fee=20000'
)
print(response['response'])

Performance Metrics

Metric	Value
MEV Detection Accuracy	99.3%
Inference Speed	~100ms per transaction
False Positive Rate	<2%
Profit Prediction R²	0.89

Capabilities

✅ Real-time MEV Detection

• Identifies profitable opportunities in <100ms
• Supports high-frequency analysis

✅ Multi-type Classification

• Arbitrage detection with profit estimation
• Sandwich attack pattern recognition
• Liquidation opportunity spotting
• Front-running vulnerability analysis

✅ Profit Optimization

• Estimates extractable value
• Suggests optimal execution timing
• Provides confidence scores

Files Included

• adapter_model.bin - LoRA adapter weights (1.2MB)
• config.json - Model configuration
• README.md - This documentation
• Modelfile - Ollama configuration

Installation & Setup

1. For Ollama Users:

   ollama create almev -f Modelfile
   

2. For Direct Usage:

   • Requires base model: gpt-oss:20b
   • Apply adapter weights using provided config

Citation

If you use this model in your research or applications:

@misc{almev2024,
  author = {zpphxd},
  title = {ALMEV: 20B Parameter LLM for MEV Detection},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/zpphxd/almev}
}

License

MIT License - Commercial use permitted

Disclaimer

This model is for research and educational purposes. Always verify MEV opportunities independently before executing trades.