Leveraging Google’s Gemma model, which is a lightweight language model, for RF signal analysis and anomaly detection. The implementation consists of two main components:

1. Fine-tuning Pipeline for Anomaly Detection

The finetune_gemma.py script sets up a custom fine-tuning workflow for Gemma, specifically targeting anomaly detection in signal data:

• Base Models: Uses “google/gemma-2b-it” by default, but configurable
• Fine-tuning Method: Implements both full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) using LoRA (Low-Rank Adaptation)
• Training Focus: Specialized for anomaly detection in log sequences
• Example Format:

The script provides options for:

• Controlling training epochs, batch size, and gradient accumulation
• Loading from preprocessed JSONL data
• Saving checkpoints during training
• Optimizing for CUDA when available

2. Data Preprocessing Pipeline

The gemma_data_preprocessor.py script handles the complex task of preparing training data by:

• Multi-modal Data Integration: Combines SDR IQ (In-phase and Quadrature) data with screen captures
• Vision LLM Processing: Uses a self-hosted vision language model to:
  • Extract OCR text from waterfall plots
  • Identify signal peaks and their characteristics
  • Detect modulation patterns (AM, FM)
  • Spot anomalies in RF signals
• Feature Engineering: Extracts rich signal features:
  • Power measurements in dB
  • Peak detection and analysis
  • Modulation indices and deviations
  • Phase characteristics
• Training Data Formatting: Creates specialized prompts that combine numerical IQ data with visual observations:

Technical Workflow

1. SDR data collection (IQ samples and screen captures)
2. Data preprocessing with gemma_data_preprocessor.py:
   • Signal feature extraction
   • Vision LLM analysis of waterfall plots
   • Training data generation
3. Model fine-tuning with finetune_gemma.py:
   • Customizes Gemma for RF anomaly detection
   • Uses LoRA for efficient training
4. The fine-tuned model presumably integrates with your broader NerfEngine system for real-time signal analysis

Key Advantages

1. Multi-modal Analysis: Combines numerical RF data with visual waterfall plot information
2. Efficiency: Uses parameter-efficient fine-tuning to adapt Gemma without excessive computational requirements
3. Specialized Prompting: Creates domain-specific prompts that encode RF signal characteristics
4. Extensible Pipeline: Modular design allows for different base models and training configurations

This implementation shows a sophisticated application of LLMs to the RF domain, creating a specialized system for signal analysis and anomaly detection by fine-tuning Gemma on domain-specific data.