{"id":2871,"date":"2025-08-16T14:23:03","date_gmt":"2025-08-16T14:23:03","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=2871"},"modified":"2025-08-16T14:41:01","modified_gmt":"2025-08-16T14:41:01","slug":"gemma-integration-in-nerfengine","status":"publish","type":"post","link":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=2871","title":{"rendered":"Gemma Integration in NerfEngine"},"content":{"rendered":"\n<h1 class=\"wp-block-heading\"><\/h1>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-opt-id=999384719  fetchpriority=\"high\" decoding=\"async\" width=\"645\" height=\"566\" src=\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:auto\/h:auto\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/08\/image-36.png\" alt=\"\" class=\"wp-image-2874\" srcset=\"https:\/\/ml6vmqguit1n.i.optimole.com\/w:645\/h:566\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/08\/image-36.png 645w, https:\/\/ml6vmqguit1n.i.optimole.com\/w:300\/h:263\/q:mauto\/f:best\/https:\/\/172-234-197-23.ip.linodeusercontent.com\/wp-content\/uploads\/2025\/08\/image-36.png 300w\" sizes=\"(max-width: 645px) 100vw, 645px\" \/><\/figure>\n\n\n\n<p>Leveraging Google&#8217;s Gemma model, which is a lightweight language model, for RF signal analysis and anomaly detection. The implementation consists of two main components:<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">1. Fine-tuning Pipeline for Anomaly Detection<\/h2>\n\n\n\n<p>The&nbsp;<a href=\"vscode-file:\/\/vscode-app\/c:\/Users\/ben\/AppData\/Local\/Programs\/Microsoft%20VS%20Code\/resources\/app\/out\/vs\/code\/electron-browser\/workbench\/workbench.html\">finetune_gemma.py<\/a>&nbsp;script sets up a custom fine-tuning workflow for Gemma, specifically targeting anomaly detection in signal data:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Base Models<\/strong>: Uses &#8220;google\/gemma-2b-it&#8221; by default, but configurable<\/li>\n\n\n\n<li><strong>Fine-tuning Method<\/strong>: Implements both full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) using LoRA (Low-Rank Adaptation)<\/li>\n\n\n\n<li><strong>Training Focus<\/strong>: Specialized for anomaly detection in log sequences<\/li>\n\n\n\n<li><strong>Example Format<\/strong>:<canvas width=\"0\" height=\"160\"><\/canvas><canvas width=\"0\" height=\"160\"><\/canvas>\n<ul class=\"wp-block-list\">\n<li><\/li>\n\n\n\n<li><\/li>\n\n\n\n<li><\/li>\n\n\n\n<li><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>The script provides options for:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Controlling training epochs, batch size, and gradient accumulation<\/li>\n\n\n\n<li>Loading from preprocessed JSONL data<\/li>\n\n\n\n<li>Saving checkpoints during training<\/li>\n\n\n\n<li>Optimizing for CUDA when available<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">2. Data Preprocessing Pipeline<\/h2>\n\n\n\n<p>The&nbsp;<a href=\"vscode-file:\/\/vscode-app\/c:\/Users\/ben\/AppData\/Local\/Programs\/Microsoft%20VS%20Code\/resources\/app\/out\/vs\/code\/electron-browser\/workbench\/workbench.html\">gemma_data_preprocessor.py<\/a>&nbsp;script handles the complex task of preparing training data by:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Multi-modal Data Integration<\/strong>: Combines SDR IQ (In-phase and Quadrature) data with screen captures<\/li>\n\n\n\n<li><strong>Vision LLM Processing<\/strong>: Uses a self-hosted vision language model to:\n<ul class=\"wp-block-list\">\n<li>Extract OCR text from waterfall plots<\/li>\n\n\n\n<li>Identify signal peaks and their characteristics<\/li>\n\n\n\n<li>Detect modulation patterns (AM, FM)<\/li>\n\n\n\n<li>Spot anomalies in RF signals<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Feature Engineering<\/strong>: Extracts rich signal features:\n<ul class=\"wp-block-list\">\n<li>Power measurements in dB<\/li>\n\n\n\n<li>Peak detection and analysis<\/li>\n\n\n\n<li>Modulation indices and deviations<\/li>\n\n\n\n<li>Phase characteristics<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Training Data Formatting<\/strong>: Creates specialized prompts that combine numerical IQ data with visual observations:<canvas width=\"0\" height=\"64\"><\/canvas><canvas width=\"0\" height=\"64\"><\/canvas>\n<ul class=\"wp-block-list\">\n<li><\/li>\n\n\n\n<li><\/li>\n\n\n\n<li><\/li>\n\n\n\n<li><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Technical Workflow<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>SDR data collection (IQ samples and screen captures)<\/li>\n\n\n\n<li>Data preprocessing with&nbsp;<a href=\"vscode-file:\/\/vscode-app\/c:\/Users\/ben\/AppData\/Local\/Programs\/Microsoft%20VS%20Code\/resources\/app\/out\/vs\/code\/electron-browser\/workbench\/workbench.html\">gemma_data_preprocessor.py<\/a>:\n<ul class=\"wp-block-list\">\n<li>Signal feature extraction<\/li>\n\n\n\n<li>Vision LLM analysis of waterfall plots<\/li>\n\n\n\n<li>Training data generation<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>Model fine-tuning with&nbsp;<a href=\"vscode-file:\/\/vscode-app\/c:\/Users\/ben\/AppData\/Local\/Programs\/Microsoft%20VS%20Code\/resources\/app\/out\/vs\/code\/electron-browser\/workbench\/workbench.html\">finetune_gemma.py<\/a>:\n<ul class=\"wp-block-list\">\n<li>Customizes Gemma for RF anomaly detection<\/li>\n\n\n\n<li>Uses LoRA for efficient training<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>The fine-tuned model presumably integrates with your broader NerfEngine system for real-time signal analysis<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Key Advantages<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Multi-modal Analysis<\/strong>: Combines numerical RF data with visual waterfall plot information<\/li>\n\n\n\n<li><strong>Efficiency<\/strong>: Uses parameter-efficient fine-tuning to adapt Gemma without excessive computational requirements<\/li>\n\n\n\n<li><strong>Specialized Prompting<\/strong>: Creates domain-specific prompts that encode RF signal characteristics<\/li>\n\n\n\n<li><strong>Extensible Pipeline<\/strong>: Modular design allows for different base models and training configurations<\/li>\n<\/ol>\n\n\n\n<p>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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Leveraging Google&#8217;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&nbsp;finetune_gemma.py&nbsp;script sets up a custom fine-tuning workflow for Gemma, specifically targeting anomaly detection in signal data: The script provides options for: 2. Data Preprocessing Pipeline&hellip;&nbsp;<a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=2871\" rel=\"bookmark\"><span class=\"screen-reader-text\">Gemma Integration in NerfEngine<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":2874,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10],"tags":[],"class_list":["post-2871","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/2871","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2871"}],"version-history":[{"count":3,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/2871\/revisions"}],"predecessor-version":[{"id":2875,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/posts\/2871\/revisions\/2875"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=\/wp\/v2\/media\/2874"}],"wp:attachment":[{"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}