{"id":105099,"date":"2026-10-30T04:02:22","date_gmt":"2026-10-30T02:02:22","guid":{"rendered":"https:\/\/twodots.gr\/?p=105099"},"modified":"2026-10-30T04:02:22","modified_gmt":"2026-10-30T02:02:22","slug":"frameft-fine-tuning-transformers-10x-ligoteres-parametroi","status":"publish","type":"post","link":"https:\/\/twodots.gr\/hr\/frameft-fine-tuning-transformers-10x-ligoteres-parametroi\/","title":{"rendered":"FrameFT: fine-tuning \u03c4\u03c9\u03bd Transformers \u03bc\u03b5 \u03ad\u03c9\u03c2 10\u00d7 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2"},"content":{"rendered":"<p class=\"td-article-lede\">The <strong>FrameFT<\/strong> \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03b1 \u03bd\u03ad\u03b1 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 parameter-efficient fine-tuning (PEFT) \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b9\u03c3\u03c4\u03ac \u03c4\u03b9\u03c2 \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03c3\u03b5\u03b9\u03c2 \u03c4\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd \u03bc\u03b5 \u03b1\u03c1\u03b1\u03b9\u03bf\u03cd\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 Fusion Frames. \u03a3\u03c4\u03b1 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b5 \u03c0\u03bf\u03bb\u03cd \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 trainable parameters \u03ba\u03b1\u03b9 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b1 \u03bc\u03b9\u03ba\u03c1\u03ac checkpoints. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7, \u03cc\u03bc\u03c9\u03c2, \u03b7 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b5\u03c1\u03ce\u03c4\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd \u00ab\u03bd\u03b9\u03ba\u03ac\u00bb \u03b3\u03b5\u03bd\u03b9\u03ba\u03ac \u03c4\u03bf LoRA, \u03b1\u03bb\u03bb\u03ac \u03b1\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b9\u03bc\u03b1 \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03c0\u03bf\u03b8\u03ae\u03ba\u03b5\u03c5\u03c3\u03b7\u03c2, \u03b4\u03b9\u03b1\u03bd\u03bf\u03bc\u03ae\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae\u03c2 \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd adapters \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c4\u03b7\u03c2 workload.<\/p>\n<div class=\"td-article-toc\">\n<div class=\"td-toc-title\">Sadr\u017eaj<\/div>\n<ul>\n<li><a href=\"#giati-fine-tuning-akrivo\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf fine-tuning \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b5 LoRA<\/a><\/li>\n<li><a href=\"#fusion-frames-kentriki-idea\">\u0397 \u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03ae \u03b9\u03b4\u03ad\u03b1: \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7 \u03b2\u03b1\u03c1\u03ce\u03bd \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 Fusion Frames<\/a><\/li>\n<li><a href=\"#spectral-tetris-koines-vaseis\">Spectral Tetris \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b1 layers<\/a><\/li>\n<li><a href=\"#frameft-glue-roberta\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf FrameFT \u03c3\u03c4\u03bf GLUE \u03bc\u03b5 RoBERTa<\/a><\/li>\n<li><a href=\"#instruction-tuning-llama-gemma\">Instruction tuning \u03c3\u03b5 Llama \u03ba\u03b1\u03b9 Gemma<\/a><\/li>\n<li><a href=\"#vision-transformers\">\u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03ba\u03b1\u03b9 \u03c3\u03b5 Vision Transformers<\/a><\/li>\n<li><a href=\"#checkpoint-throughput\">Checkpoint 1,28 MB \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf adapter throughput<\/a><\/li>\n<li><a href=\"#statherotita-convergence\">\u03a3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 convergence: \u03c4\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9<\/a><\/li>\n<li><a href=\"#epicheirimatiko-ai-stack\">\u03a0\u03bf\u03cd \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf FrameFT \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc AI stack<\/a><\/li>\n<li><a href=\"#periorismoi-poc\">\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 proof of concept<\/a><\/li>\n<li><a href=\"#vhmata-frameft-pilot\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 FrameFT pilot<\/a><\/li>\n<li><a href=\"#symperasma\">\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b3\u03b9\u03b1 decision-makers<\/a><\/li>\n<\/ul>\n<\/div>\n<p>\u03a4\u03bf fine-tuning \u03b5\u03bd\u03cc\u03c2 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b6\u03ae\u03c4\u03b7\u03bc\u03b1 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03af\u03c1\u03b7\u03c3\u03b7 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 \u03af\u03b4\u03b9\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03b3\u03b9\u03b1 \u03c0\u03b5\u03bb\u03ac\u03c4\u03b5\u03c2, \u03b1\u03b3\u03bf\u03c1\u03ad\u03c2 \u03ae \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2, \u03c4\u03bf \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03c0\u03c1\u03cc\u03b2\u03bb\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ac \u03c0\u03b9\u03bf \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03bf: \u03c0\u03cc\u03c3\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c3\u03b5\u03b9, \u03c0\u03cc\u03c3\u03b1 \u03b1\u03c1\u03c7\u03b5\u03af\u03b1 adapter \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c3\u03bf \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03be\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03bc\u03af\u03b1 \u03b5\u03be\u03b5\u03b9\u03b4\u03af\u03ba\u03b5\u03c5\u03c3\u03b7 \u03c3\u03c4\u03b7\u03bd \u03ac\u03bb\u03bb\u03b7.<\/p>\n<p>\u0397 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b4\u03b5\u03bd \u03c5\u03c0\u03cc\u03c3\u03c7\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u00ab\u03bc\u03b1\u03b3\u03b9\u03ba\u03cc\u00bb \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u03a0\u03c1\u03bf\u03c4\u03b5\u03af\u03bd\u03b5\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ae \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7\u03c2 \u03c4\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd: \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af <em>Fusion Frames<\/em>, \u03b4\u03b7\u03bb\u03b1\u03b4\u03ae \u03bf\u03c1\u03b3\u03b1\u03bd\u03c9\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03b4\u03c5\u03bd\u03b7\u03c4\u03b9\u03ba\u03ac \u03b5\u03c0\u03b9\u03ba\u03b1\u03bb\u03c5\u03c0\u03c4\u03cc\u03bc\u03b5\u03bd\u03b1 \u03c5\u03c0\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1, \u03ba\u03b1\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03ad\u03bd\u03b1\u03bd \u03c0\u03bf\u03bb\u03cd \u03b1\u03c1\u03b1\u03b9\u03cc \u03c0\u03af\u03bd\u03b1\u03ba\u03b1 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ce\u03bd. \u03a3\u03c4\u03b7\u03bd \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c4\u03c9\u03bd Harshavardhan Adepu, Li Zhang, Sanjiv Kumar \u03ba\u03b1\u03b9 Vikas Singh, \u03c4\u03bf FrameFT \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2, instruction tuning \u03ba\u03b1\u03b9 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7 \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd. \u03a4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03b9\u03bd\u03b7\u03b8\u03b5\u03af \u03c3\u03c4\u03bf \u03b5\u03c0\u03af\u03c0\u03b5\u03b4\u03bf \u03ba\u03b1\u03b8\u03b9\u03b5\u03c1\u03c9\u03bc\u03ad\u03bd\u03c9\u03bd PEFT \u03bc\u03b5\u03b8\u03cc\u03b4\u03c9\u03bd \u03bc\u03b5 \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 trainable parameters, \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 checkpoints \u03ba\u03b1\u03b9 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf throughput \u03c4\u03bf\u03c5 adapter layer \u03c3\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf hardware.<\/p>\n<h2 id=\"giati-fine-tuning-akrivo\">\u0393\u03b9\u03b1\u03c4\u03af \u03c4\u03bf fine-tuning \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03ba\u03c1\u03b9\u03b2\u03cc \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b5 LoRA<\/h2>\n<p>\u03a4\u03bf \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 fine-tuning \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03bd\u03b5\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03bf \u03bc\u03ad\u03c1\u03bf\u03c2 \u03ae \u03ba\u03b1\u03b9 \u03c4\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c4\u03c9\u03bd \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd \u03b5\u03bd\u03cc\u03c2 foundation model. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03cd\u03c3\u03ba\u03bf\u03bb\u03bf \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf \u03ad\u03c7\u03b5\u03b9 \u03b4\u03b9\u03c3\u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03ba\u03cc\u03bc\u03b7 \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03cc\u03c4\u03b5\u03c1\u03bf \u03cc\u03c4\u03b1\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2. \u039f\u03b9 PEFT \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03af\u03c9\u03c3\u03b1\u03bd \u03b4\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03b1\u03c5\u03c4\u03cc \u03c4\u03bf \u03b2\u03ac\u03c1\u03bf\u03c2. \u0397 \u03c0\u03b9\u03bf \u03b3\u03bd\u03c9\u03c3\u03c4\u03ae, \u03c4\u03bf LoRA, \u03ba\u03c1\u03b1\u03c4\u03ac \u03c4\u03b1 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ac \u03b2\u03ac\u03c1\u03b7 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03b4\u03cd\u03bf low-rank \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03bf layer.<\/p>\n<p>\u0397 \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03c4\u03bf\u03c5 LoRA \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b5\u03b3\u03ac\u03bb\u03b7, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c4\u03bf\u03c5 \u03b5\u03be\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bd\u03b1 \u03ba\u03bb\u03b9\u03bc\u03b1\u03ba\u03ce\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03b7\u03bd \u03ba\u03c1\u03c5\u03c6\u03ae \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03c4\u03bf rank. \u0395\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd, \u03bf\u03b9 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c5\u03ba\u03bd\u03bf\u03af. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b5\u03c0\u03b9\u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b1\u03c5\u03c4\u03cc \u03b5\u03c0\u03b7\u03c1\u03b5\u03ac\u03b6\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03bf inference throughput, \u03b5\u03b9\u03b4\u03b9\u03ba\u03ac \u03c3\u03b5 multi-tenant \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03cc\u03c0\u03bf\u03c5 \u03ad\u03bd\u03b1 \u03ba\u03bf\u03b9\u03bd\u03cc backbone \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03b5\u03af \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 fine-tuned \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2. \u0395\u03ba\u03b5\u03af \u03b4\u03b5\u03bd \u03b1\u03c1\u03ba\u03b5\u03af \u03ad\u03bd\u03b1 adapter \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf\u00bb. \u03a0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03bc\u03b9\u03ba\u03c1\u03cc \u03ba\u03b1\u03b9 \u03b3\u03c1\u03ae\u03b3\u03bf\u03c1\u03bf \u03ce\u03c3\u03c4\u03b5 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03b5\u03c4\u03b1\u03b9, \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c6\u03ad\u03c1\u03b5\u03c4\u03b1\u03b9 \u03ba\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03b9\u03b5\u03af\u03c4\u03b1\u03b9 \u03c7\u03c9\u03c1\u03af\u03c2 \u03bd\u03b1 \u03b3\u03af\u03bd\u03b5\u03c4\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf bottleneck.<\/p>\n<p>\u039f\u03b9 sparse fine-tuning \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03b9 \u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03bf\u03cd\u03bd \u03ac\u03bb\u03bb\u03bf \u03b4\u03c1\u03cc\u03bc\u03bf: \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03bd\u03bf\u03c5\u03bd \u03ad\u03bd\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc \u03c5\u03c0\u03bf\u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c4\u03c9\u03bd \u03b1\u03c1\u03c7\u03b9\u03ba\u03ce\u03bd \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd. \u038c\u03bc\u03c9\u03c2 \u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b1\u03c5\u03c4\u03bf\u03cd \u03c4\u03bf\u03c5 \u03c5\u03c0\u03bf\u03c3\u03c5\u03bd\u03cc\u03bb\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af pruning, gradients, Fisher information \u03ae \u03ac\u03bb\u03bb\u03b7 \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7. \u03a3\u03cd\u03bc\u03c6\u03c9\u03bd\u03b1 \u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2, \u03b1\u03c5\u03c4\u03ae \u03b7 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03c5\u03c3\u03ba\u03bf\u03bb\u03b5\u03cd\u03b5\u03b9 \u03c4\u03b7\u03bd \u03ba\u03bb\u03b9\u03bc\u03ac\u03ba\u03c9\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03b7 \u03c1\u03cd\u03b8\u03bc\u03b9\u03c3\u03b7 \u03c4\u03c9\u03bd hyperparameters.<\/p>\n<div class=\"td-comparison-cards\">\n<article class=\"td-platform-card\">\n<h3>LoRA<\/h3>\n<p>\u039c\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03b4\u03cd\u03bf \u03c0\u03c5\u03ba\u03bd\u03bf\u03cd\u03c2 low-rank \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03b1\u03bd\u03ac \u03b5\u03c0\u03b9\u03bb\u03b5\u03b3\u03bc\u03ad\u03bd\u03bf layer. \u0394\u03b9\u03b1\u03b8\u03ad\u03c4\u03b5\u03b9 \u03ce\u03c1\u03b9\u03bc\u03bf \u03bf\u03b9\u03ba\u03bf\u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03af \u03c4\u03bf \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03cc baseline \u03b3\u03b9\u03b1 \u03bd\u03ad\u03b1 PEFT \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1, \u03b1\u03bb\u03bb\u03ac \u03c4\u03bf task-specific state \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03c4\u03b7 \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03c4\u03bf rank.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">\u038f\u03c1\u03b9\u03bc\u03bf baseline<\/span><span class=\"td-badge\">Dense adapters<\/span><\/div>\n<\/article>\n<article class=\"td-platform-card td-platform-card--navy\">\n<h3>FrameFT<\/h3>\n<p>\u039a\u03c1\u03b1\u03c4\u03ac \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ad\u03c2, \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 Fusion Frames \u03ba\u03b1\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03b1\u03c1\u03b1\u03b9\u03bf\u03cd\u03c2 coefficient matrices. \u03a3\u03c4\u03bf\u03c7\u03b5\u03cd\u03b5\u03b9 \u03c3\u03b5 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf checkpoint \u03ba\u03b1\u03b9 \u03b3\u03c1\u03b7\u03b3\u03bf\u03c1\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03c9\u03bd.<\/p>\n<div class=\"td-badge-row\"><span class=\"td-badge\">Sparse coefficients<\/span><span class=\"td-badge\">Shared bases<\/span><\/div>\n<\/article>\n<\/div>\n<h2 id=\"fusion-frames-kentriki-idea\">\u0397 \u03ba\u03b5\u03bd\u03c4\u03c1\u03b9\u03ba\u03ae \u03b9\u03b4\u03ad\u03b1: \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7 \u03b2\u03b1\u03c1\u03ce\u03bd \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 Fusion Frames<\/h2>\n<p>\u03a4\u03b1 frames \u03b3\u03b5\u03bd\u03b9\u03ba\u03b5\u03cd\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03ad\u03bd\u03bd\u03bf\u03b9\u03b1 \u03bc\u03b9\u03b1\u03c2 \u03b2\u03ac\u03c3\u03b7\u03c2: \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03bf\u03c5\u03bd \u03c0\u03bb\u03b5\u03bf\u03bd\u03b1\u03c3\u03bc\u03cc \u03c3\u03c4\u03b7\u03bd \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03b5\u03bd\u03cc\u03c2 \u03b4\u03b9\u03b1\u03bd\u03c5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd \u03c7\u03ce\u03c1\u03bf\u03c5, \u03ba\u03ac\u03c4\u03b9 \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03c0\u03c1\u03bf\u03c3\u03c6\u03ad\u03c1\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03b5\u03c5\u03b5\u03bb\u03b9\u03be\u03af\u03b1. \u03a4\u03b1 Fusion Frames \u03b5\u03c0\u03b5\u03ba\u03c4\u03b5\u03af\u03bd\u03bf\u03c5\u03bd \u03c4\u03b7\u03bd \u03b9\u03b4\u03ad\u03b1 \u03c3\u03b5 \u03c3\u03c5\u03bb\u03bb\u03bf\u03b3\u03ad\u03c2 \u03c5\u03c0\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd. \u0391\u03bd\u03c4\u03af, \u03bb\u03bf\u03b9\u03c0\u03cc\u03bd, \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03b7\u03bd \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7 \u03b2\u03b1\u03c1\u03ce\u03bd \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd low-rank \u03c7\u03ce\u03c1\u03bf \u03c0\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c0\u03c4\u03b5\u03b9 \u03ad\u03bc\u03bc\u03b5\u03c3\u03b1 \u03b1\u03c0\u03cc \u03b4\u03cd\u03bf \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2, \u03c4\u03bf FrameFT \u03bf\u03c1\u03b3\u03b1\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c1\u03b7\u03c4\u03ac \u03c4\u03bf\u03bd \u03c7\u03ce\u03c1\u03bf \u03b5\u03b9\u03c3\u03cc\u03b4\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd \u03c7\u03ce\u03c1\u03bf \u03b5\u03be\u03cc\u03b4\u03bf\u03c5 \u03c3\u03b5 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03ac \u03c5\u03c0\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1.<\/p>\n<p>\u0393\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 layer, \u03bf\u03b9 \u03c3\u03c7\u03ad\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b1 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b1 \u03c5\u03c0\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03ba\u03c9\u03b4\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03bc\u03b9\u03ba\u03c1\u03bf\u03cd\u03c2 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ce\u03bd. \u0391\u03c5\u03c4\u03bf\u03af \u03bc\u03c0\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03c3\u03b5 block-diagonal \u03b4\u03bf\u03bc\u03ae \u03ba\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03ad\u03bd\u03b1 \u03b1\u03c1\u03b1\u03b9\u03cc \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c4\u03c9\u03bd \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ce\u03bd \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 trainable. \u039f\u03b9 projection matrices \u03c4\u03c9\u03bd Fusion Frames \u03bc\u03ad\u03bd\u03bf\u03c5\u03bd \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03bf\u03af. \u0388\u03c4\u03c3\u03b9 \u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03c0\u03c1\u03bf\u03c3\u03c0\u03b1\u03b8\u03b5\u03af \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b4\u03c5\u03ac\u03c3\u03b5\u03b9 \u03b4\u03cd\u03bf \u03b9\u03b4\u03b9\u03cc\u03c4\u03b7\u03c4\u03b5\u03c2: \u03c4\u03b7\u03bd \u03c0\u03b1\u03b3\u03ba\u03cc\u03c3\u03bc\u03b9\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1 \u03bc\u03b9\u03b1\u03c2 subspace \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5 \u03cc\u03c0\u03c9\u03c2 \u03c4\u03bf LoRA \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1 \u03b5\u03bd\u03cc\u03c2 sparse update.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2, \u03b7 \u03bf\u03c5\u03c3\u03af\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf task-specific \u03bc\u03ad\u03c1\u03bf\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf\u03c5\u03c2 \u03b1\u03c1\u03b1\u03b9\u03bf\u03cd\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2. \u0397 \u00ab\u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03af\u03b1\u00bb \u03c4\u03b7\u03c2 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7\u03c2 \u03c0\u03b1\u03c1\u03ac\u03b3\u03b5\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03b1\u03bb\u03b3\u03cc\u03c1\u03b9\u03b8\u03bc\u03bf \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b1\u03bd\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03c4\u03b5\u03af \u03ae \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03c5\u03c4\u03b5\u03af \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 layer. \u0391\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03cc\u03c0\u03bf\u03c5 \u03bc\u03b9\u03b1 \u03bc\u03b1\u03b8\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03c0\u03bb\u03b5\u03bf\u03bd\u03ad\u03ba\u03c4\u03b7\u03bc\u03b1.<\/p>\n<h2 id=\"spectral-tetris-koines-vaseis\">Spectral Tetris \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b1 layers<\/h2>\n<p>\u03a4\u03bf FrameFT \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af Tight Fusion Frames \u03c0\u03bf\u03c5 \u03ba\u03b1\u03c4\u03b1\u03c3\u03ba\u03b5\u03c5\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ac. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03b5\u03c1\u03b9\u03b3\u03c1\u03ac\u03c6\u03b5\u03b9 \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03b2\u03b1\u03c3\u03b9\u03c3\u03bc\u03ad\u03bd\u03b7 \u03c3\u03c4\u03bf Spectral Tetris \u03ba\u03b1\u03b9 \u03b5\u03c0\u03ad\u03ba\u03c4\u03b1\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03bc\u03b9\u03b3\u03b1\u03b4\u03b9\u03ba\u03cc \u03c3\u03c4\u03bf\u03bd \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03c7\u03ce\u03c1\u03bf. \u039f\u03b9 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b5\u03be\u03b1\u03c1\u03c4\u03ce\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c4\u03bf\u03c5 layer, \u03cc\u03c7\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf \u03c0\u03b5\u03c1\u03b9\u03b5\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf \u03c4\u03c9\u03bd \u03c0\u03c1\u03bf\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03c9\u03bd \u03b2\u03b1\u03c1\u03ce\u03bd. \u0395\u03c0\u03bf\u03bc\u03ad\u03bd\u03c9\u03c2, layers \u03bc\u03b5 \u03c4\u03b9\u03c2 \u03af\u03b4\u03b9\u03b5\u03c2 \u03b4\u03b9\u03b1\u03c3\u03c4\u03ac\u03c3\u03b5\u03b9\u03c2 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf frames.<\/p>\n<p>\u0391\u03c5\u03c4\u03cc \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b1\u03c0\u03cc SVD-based \u03c0\u03c1\u03bf\u03c3\u03b5\u03b3\u03b3\u03af\u03c3\u03b5\u03b9\u03c2, \u03c3\u03c4\u03b9\u03c2 \u03bf\u03c0\u03bf\u03af\u03b5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03c0\u03b1\u03b9\u03c4\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac dense basis vectors \u03b3\u03b9\u03b1 \u03ba\u03ac\u03b8\u03b5 layer. \u03a3\u03c4\u03bf FrameFT, \u03bf\u03b9 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03b1\u03ba\u03b1\u03c4\u03b1\u03c3\u03ba\u03b5\u03c5\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ac \u03ba\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bf\u03b9 \u03b1\u03c1\u03b1\u03b9\u03bf\u03af \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03c5\u03c4\u03bf\u03cd\u03bd. \u0397 \u03ba\u03b1\u03c4\u03b1\u03c3\u03ba\u03b5\u03c5\u03ae \u03ad\u03c7\u03b5\u03b9 \u03b5\u03c0\u03b9\u03c0\u03bb\u03ad\u03bf\u03bd block sparsity, \u03ac\u03c1\u03b1 \u03bf\u03b9 \u03c0\u03bf\u03bb\u03bb\u03b1\u03c0\u03bb\u03b1\u03c3\u03b9\u03b1\u03c3\u03bc\u03bf\u03af \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03b9\u03ba\u03cc\u03c4\u03b5\u03c1\u03bf\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b9\u03c2 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03c2 dense \u03c0\u03c1\u03ac\u03be\u03b5\u03b9\u03c2.<\/p>\n<p>\u0397 \u03ba\u03bf\u03b9\u03bd\u03ae \u03c7\u03c1\u03ae\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bb\u03ce\u03c2 \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7 \u03c4\u03c9\u03bd \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03ad\u03c9\u03bd. \u03a3\u03b5 ablation study \u03c3\u03c4\u03bf RoBERTa-Base\/GLUE, \u03ad\u03bd\u03b1 \u03ba\u03bf\u03b9\u03bd\u03cc \u03c4\u03c5\u03c7\u03b1\u03af\u03bf mask \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b1 layers \u03ad\u03b4\u03c9\u03c3\u03b5 \u03bc\u03ad\u03c3\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 86,18 \u00b1 0,18, \u03b5\u03bd\u03ce \u03be\u03b5\u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03cc \u03c4\u03c5\u03c7\u03b1\u03af\u03bf mask \u03b1\u03bd\u03ac layer \u03ad\u03b4\u03c9\u03c3\u03b5 86,26 \u00b1 0,17. \u039f\u03b9 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b5\u03bd\u03c4\u03cc\u03c2 \u03c4\u03b7\u03c2 \u03b4\u03b7\u03bb\u03c9\u03bc\u03ad\u03bd\u03b7\u03c2 \u03b4\u03b9\u03b1\u03ba\u03cd\u03bc\u03b1\u03bd\u03c3\u03b7\u03c2. \u0391\u03bd\u03c4\u03af\u03b8\u03b5\u03c4\u03b1, \u03c4\u03bf gradient-based mask \u03ad\u03c0\u03b5\u03c3\u03b5 \u03c3\u03b5 \u03bc\u03ad\u03c3\u03bf 74,83. \u03a3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf setup, \u03b7 \u03c0\u03b9\u03bf \u03c0\u03b5\u03c1\u03af\u03c0\u03bb\u03bf\u03ba\u03b7 \u03c0\u03c1\u03bf\u03c3\u03b1\u03c1\u03bc\u03bf\u03c3\u03c4\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03b4\u03b5\u03bd \u03ad\u03c6\u03b5\u03c1\u03b5 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1.<\/p>\n<div class=\"td-article-note\"><strong>\u03a3\u03c9\u03c3\u03c4\u03ae \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 ablation:<\/strong> \u03c4\u03bf \u03c0\u03b5\u03af\u03c1\u03b1\u03bc\u03b1 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf shared random mask \u03bb\u03b5\u03b9\u03c4\u03bf\u03cd\u03c1\u03b3\u03b7\u03c3\u03b5 \u03cc\u03c3\u03bf \u03c4\u03bf per-layer mask \u03c3\u03c4\u03bf \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03bf RoBERTa\/GLUE setup. \u0394\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, task \u03ae sparsity pattern \u03b8\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03c4\u03b7\u03bd \u03af\u03b4\u03b9\u03b1 \u03c3\u03c5\u03bc\u03c0\u03b5\u03c1\u03b9\u03c6\u03bf\u03c1\u03ac.<\/div>\n<h2 id=\"frameft-glue-roberta\">\u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b5 \u03c4\u03bf FrameFT \u03c3\u03c4\u03bf GLUE \u03bc\u03b5 RoBERTa<\/h2>\n<p>\u03a3\u03c4\u03bf RoBERTa Base, \u03c4\u03bf FrameFT \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 24 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 trainable parameters \u03ba\u03b1\u03b9 \u03c0\u03ad\u03c4\u03c5\u03c7\u03b5 \u03bc\u03ad\u03c3\u03bf \u03c3\u03ba\u03bf\u03c1 86,1 \u03c3\u03c4\u03b9\u03c2 \u03ad\u03be\u03b9 \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03c4\u03bf\u03c5 GLUE. \u03a4\u03bf LoRA, \u03bc\u03b5 0,3 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03b5\u03af\u03c7\u03b5 \u03bc\u03ad\u03c3\u03bf 85,2. \u0397 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b4\u03b9\u03b1\u03b2\u03b1\u03c3\u03c4\u03b5\u03af \u03c9\u03c2 \u03ba\u03b1\u03b8\u03bf\u03bb\u03b9\u03ba\u03ae \u03c5\u03c0\u03b5\u03c1\u03bf\u03c7\u03ae \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 task: \u03c4\u03bf LoRA \u03b5\u03af\u03c7\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03c4\u03b9\u03bc\u03ae \u03c3\u03c4\u03bf SST-2 \u03ba\u03b1\u03b9 \u03c4\u03bf QNLI, \u03b5\u03bd\u03ce \u03c4\u03bf FrameFT \u03be\u03b5\u03c7\u03ce\u03c1\u03b9\u03c3\u03b5 \u03c3\u03c4\u03bf MRPC \u03ba\u03b1\u03b9 \u03c4\u03bf CoLA. \u03a4\u03bf \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03cc \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03c3\u03c5\u03bd\u03b4\u03c5\u03b1\u03c3\u03bc\u03cc\u03c2 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae\u03c2 \u03bc\u03ad\u03c3\u03b7\u03c2 \u03b5\u03c0\u03af\u03b4\u03bf\u03c3\u03b7\u03c2 \u03ba\u03b1\u03b9 \u03c0\u03bf\u03bb\u03cd \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c5 trainable \u03c3\u03c5\u03bd\u03cc\u03bb\u03bf\u03c5.<\/p>\n<p>\u03a3\u03c4\u03bf RoBERTa Large, \u03c4\u03bf FrameFT \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b5 48 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2 \u03ba\u03b1\u03b9 \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03bc\u03ad\u03c3\u03bf 88,7. \u03a4\u03bf LoRA \u03b5\u03af\u03c7\u03b5 88,2 \u03bc\u03b5 0,8 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03b5\u03bd\u03ce \u03c4\u03bf LoRA-XS \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 88,7 \u03bc\u03b5 60 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2. \u0386\u03c1\u03b1 \u03c4\u03bf \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf FrameFT \u00ab\u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03c0\u03b1\u03bd\u03c4\u03bf\u03cd\u00bb, \u03b1\u03bb\u03bb\u03ac \u03cc\u03c4\u03b9 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf \u03b1\u03bd\u03ce\u03c4\u03b5\u03c1\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03c4\u03c9\u03bd \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03c9\u03bd \u03bc\u03b5 \u03b5\u03be\u03b1\u03b9\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c7\u03b1\u03bc\u03b7\u03bb\u03cc parameter budget.<\/p>\n<p>\u039f\u03b9 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03ba\u03b1\u03bb\u03cd\u03c0\u03c4\u03bf\u03c5\u03bd sentiment classification, \u03b1\u03bd\u03af\u03c7\u03bd\u03b5\u03c5\u03c3\u03b7 \u03c0\u03b1\u03c1\u03b1\u03c6\u03c1\u03ac\u03c3\u03b5\u03c9\u03bd, \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ae \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae, natural-language inference \u03ba\u03b1\u03b9 semantic similarity. \u0391\u03c5\u03c4\u03cc \u03b4\u03af\u03bd\u03b5\u03b9 \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b5\u03cd\u03c1\u03bf\u03c2 \u03b1\u03c0\u03cc \u03bc\u03b9\u03b1 \u03bc\u03b5\u03bc\u03bf\u03bd\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ae, \u03c7\u03c9\u03c1\u03af\u03c2 \u03cc\u03bc\u03c9\u03c2 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03b1\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03bf GLUE \u03c3\u03b5 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b9\u03ba\u03cc dataset. \u039a\u03ac\u03b8\u03b5 \u03bf\u03c1\u03b3\u03b1\u03bd\u03b9\u03c3\u03bc\u03cc\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03b4\u03b9\u03ba\u03cc \u03c4\u03bf\u03c5 validation set, \u03bc\u03b5 \u03b4\u03b5\u03af\u03b3\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03c5 \u03b1\u03bd\u03c4\u03b9\u03c0\u03c1\u03bf\u03c3\u03c9\u03c0\u03b5\u03cd\u03bf\u03c5\u03bd \u03c4\u03b9\u03c2 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ad\u03c2 \u03c0\u03c1\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2, \u03b3\u03bb\u03ce\u03c3\u03c3\u03b5\u03c2 \u03ba\u03b1\u03b9 failure modes \u03c4\u03bf\u03c5 \u03c0\u03c1\u03bf\u03ca\u03cc\u03bd\u03c4\u03bf\u03c2.<\/p>\n<h2 id=\"instruction-tuning-llama-gemma\">Instruction tuning \u03c3\u03b5 Llama \u03ba\u03b1\u03b9 Gemma<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af instruction tuning \u03c3\u03b5 Llama-2-7B, Llama-2-13B, Gemma-2-2B, Gemma-2-9B \u03ba\u03b1\u03b9 Llama-3.1-8B. \u039f\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03bf\u03c5\u03bd LoRA, DoRA, IA3, S2FT, SVFT \u03ba\u03b1\u03b9 FourierFT. \u03a4\u03bf FrameFT \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b1\u03c0\u03cc 260 \u03ad\u03c9\u03c2 420 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 trainable parameters \u03c3\u03c4\u03b1 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1 \u03c0\u03bf\u03c5 \u03b5\u03bc\u03c6\u03b1\u03bd\u03af\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf\u03c5\u03c2 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2, \u03b5\u03bd\u03ce \u03c4\u03bf LoRA \u03ba\u03b1\u03b9 \u03c4\u03bf DoRA \u03b2\u03c1\u03af\u03c3\u03ba\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ac \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1.<\/p>\n<p>\u03a3\u03c4\u03bf Gemma-2-2B, \u03c4\u03bf FrameFT \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03bc\u03ad\u03c3\u03bf 64,11 \u03bc\u03b5 260 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 62,70 \u03b3\u03b9\u03b1 LoRA \u03bc\u03b5 6,4 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1 \u03ba\u03b1\u03b9 63,20 \u03b3\u03b9\u03b1 DoRA \u03bc\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf parameter count. \u03a3\u03c4\u03bf Gemma-2-9B, \u03c4\u03bf FrameFT \u03ad\u03c6\u03c4\u03b1\u03c3\u03b5 70,65 \u03bc\u03b5 420 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03c0\u03bf\u03bb\u03cd \u03ba\u03bf\u03bd\u03c4\u03ac \u03c3\u03c4\u03bf 70,83 \u03c4\u03bf\u03c5 LoRA \u03bc\u03b5 17,9 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1. \u03a3\u03c4\u03bf Llama-2-13B, \u03c4\u03bf FrameFT \u03b5\u03af\u03c7\u03b5 66,11 \u03bc\u03b5 400 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 66,42 \u03b3\u03b9\u03b1 LoRA \u03ba\u03b1\u03b9 66,58 \u03b3\u03b9\u03b1 DoRA \u03bc\u03b5 26,2 \u03b5\u03ba\u03b1\u03c4\u03bf\u03bc\u03bc\u03cd\u03c1\u03b9\u03b1.<\/p>\n<p>\u0391\u03c5\u03c4\u03ac \u03c4\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ac \u03b3\u03b9\u03b1 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03cc\u03c7\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf fine-tuned \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u0397 \u03bc\u03b9\u03ba\u03c1\u03ae \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03c4\u03bf benchmark \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03b5\u03ba\u03c4\u03ae \u03b1\u03bd \u03bc\u03b5\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c3\u03b7\u03bc\u03b1\u03bd\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf storage, \u03c4\u03bf distribution \u03ba\u03b1\u03b9 \u03c4\u03bf\u03bd \u03c7\u03c1\u03cc\u03bd\u03bf \u03b1\u03bb\u03bb\u03b1\u03b3\u03ae\u03c2 adapter. \u0391\u03bd\u03c4\u03af\u03c3\u03c4\u03c1\u03bf\u03c6\u03b1, \u03c3\u03b5 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2 \u03c5\u03c8\u03b7\u03bb\u03bf\u03cd \u03c1\u03af\u03c3\u03ba\u03bf\u03c5, \u03b1\u03ba\u03cc\u03bc\u03b7 \u03ba\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03ae \u03c0\u03c4\u03ce\u03c3\u03b7 \u03c3\u03b5 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b5\u03c2 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c0\u03b1\u03b3\u03bf\u03c1\u03b5\u03c5\u03c4\u03b9\u03ba\u03ae. \u0397 \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af metrics \u03b1\u03bd\u03ac use case, \u03cc\u03c7\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf\u03bd \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf \u03b5\u03bd\u03cc\u03c2 \u03b1\u03ba\u03b1\u03b4\u03b7\u03bc\u03b1\u03ca\u03ba\u03bf\u03cd \u03c0\u03af\u03bd\u03b1\u03ba\u03b1.<\/p>\n<h2 id=\"vision-transformers\">\u03a4\u03bf \u03af\u03b4\u03b9\u03bf \u03bc\u03bf\u03c4\u03af\u03b2\u03bf \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b7\u03ba\u03b5 \u03ba\u03b1\u03b9 \u03c3\u03b5 Vision Transformers<\/h2>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03c4\u03b7 \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1. \u0395\u03c6\u03ac\u03c1\u03bc\u03bf\u03c3\u03b1\u03bd \u03c4\u03bf FrameFT \u03c3\u03b5 ViT Base \u03ba\u03b1\u03b9 ViT Large, \u03c0\u03c1\u03bf\u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03c3\u03c4\u03bf ImageNet-21K, \u03ba\u03b1\u03b9 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b1\u03bd \u03bf\u03ba\u03c4\u03ce \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd. \u03a4\u03bf \u03c3\u03cd\u03bd\u03bf\u03bb\u03bf \u03c0\u03b5\u03c1\u03b9\u03bb\u03ac\u03bc\u03b2\u03b1\u03bd\u03b5 Oxford Pets, Stanford Cars, FGVC Aircraft, CIFAR10, CIFAR100, DTD \u03b3\u03b9\u03b1 textures, EuroSAT \u03ba\u03b1\u03b9 RESISC45 \u03b3\u03b9\u03b1 remote sensing.<\/p>\n<p>\u0397 \u03c0\u03bf\u03b9\u03ba\u03b9\u03bb\u03af\u03b1 \u03c4\u03c9\u03bd datasets \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03bc\u03b5 Fusion Frames \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b4\u03b5\u03bc\u03ad\u03bd\u03b7 \u03b1\u03c0\u03bf\u03ba\u03bb\u03b5\u03b9\u03c3\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03c4\u03bf \u03ba\u03b5\u03af\u03bc\u03b5\u03bd\u03bf. \u03a9\u03c3\u03c4\u03cc\u03c3\u03bf, \u03ad\u03bd\u03b1 \u03ba\u03b1\u03bb\u03cc \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03c3\u03b5 image classification \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03b1\u03c5\u03c4\u03cc\u03bc\u03b1\u03c4\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bb\u03bb\u03b7\u03bb\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b3\u03b9\u03b1 detection, segmentation \u03ae multimodal generation. \u0391\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03b9 \u03bc\u03cc\u03bd\u03bf \u03cc\u03c4\u03b9 \u03b7 \u03b2\u03b1\u03c3\u03b9\u03ba\u03ae \u03b9\u03b4\u03ad\u03b1 \u03b5\u03c6\u03b1\u03c1\u03bc\u03cc\u03c3\u03c4\u03b7\u03ba\u03b5 \u03bc\u03b5 \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03af\u03b1 \u03c3\u03b5 \u03c0\u03b5\u03c1\u03b9\u03c3\u03c3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03b1\u03c0\u03cc \u03bc\u03af\u03b1 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 Transformer.<\/p>\n<p>\u0393\u03b9\u03b1 e-commerce \u03ae retail \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2, \u03c4\u03bf \u03b5\u03bd\u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03bf\u03bd \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2 \u03cc\u03c0\u03c9\u03c2 product categorization, visual similarity \u03ba\u03b1\u03b9 quality control. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b4\u03b5\u03bd \u03bc\u03b5\u03c4\u03c1\u03ac \u03b1\u03c5\u03c4\u03ac \u03c4\u03b1 workflows \u03c9\u03c2 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03c3\u03c5\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1. \u039c\u03c0\u03bf\u03c1\u03b5\u03af \u03cc\u03bc\u03c9\u03c2 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03b5\u03b9 \u03b1\u03c6\u03b5\u03c4\u03b7\u03c1\u03af\u03b1 \u03b3\u03b9\u03b1 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03bf proof of concept \u03bc\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1 \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ad\u03c2 baseline.<\/p>\n<h2 id=\"checkpoint-throughput\">Checkpoint 1,28 MB \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf adapter throughput<\/h2>\n<p>\u03a4\u03bf \u03c0\u03b9\u03bf \u03ac\u03bc\u03b5\u03c3\u03b1 \u03ba\u03b1\u03c4\u03b1\u03bd\u03bf\u03b7\u03c4\u03cc \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03cc \u03b5\u03cd\u03c1\u03b7\u03bc\u03b1 \u03b1\u03c6\u03bf\u03c1\u03ac \u03c4\u03bf \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 \u03c4\u03bf\u03c5 adapter. \u0393\u03b9\u03b1 Llama-2-7B, \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd checkpoint 1,28 MB \u03b3\u03b9\u03b1 FrameFT, \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 67,1 MB \u03b3\u03b9\u03b1 LoRA rank-32, \u03bc\u03b5 \u03c4\u03bf\u03c5\u03c2 \u03b4\u03cd\u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03cd\u03c2 \u03c3\u03b5 FP32. \u0395\u03c0\u03b5\u03b9\u03b4\u03ae \u03c4\u03b1 Fusion Frames \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ac, \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03bf\u03bd\u03c4\u03b1\u03b9 basis matrices. \u03a3\u03ce\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03bf\u03b9 \u03b1\u03c1\u03b1\u03b9\u03bf\u03af coefficient matrices.<\/p>\n<p>\u03a3\u03c4\u03bf throughput \u03c4\u03bf\u03c5 adapter layer, \u03c3\u03b5 NVIDIA A100, \u03bf \u03c0\u03af\u03bd\u03b1\u03ba\u03b1\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 39,4 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf \u03b3\u03b9\u03b1 FrameFT \u03ba\u03b1\u03b9 23,6 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03b3\u03b9\u03b1 LoRA \u03c3\u03c4\u03bf Llama-2-7B. \u03a3\u03c4\u03bf Gemma-2-9B \u03bf\u03b9 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03c2 \u03c4\u03b9\u03bc\u03ad\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 29,6 \u03ba\u03b1\u03b9 22,1 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2, \u03b5\u03bd\u03ce \u03c3\u03c4\u03bf Llama-3.1-8B \u03b5\u03af\u03bd\u03b1\u03b9 27,3 \u03ba\u03b1\u03b9 22,3 \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2. \u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b4\u03b9\u03b5\u03c5\u03ba\u03c1\u03b9\u03bd\u03af\u03b6\u03bf\u03c5\u03bd \u03cc\u03c4\u03b9 \u03bc\u03b5\u03c4\u03c1\u03bf\u03cd\u03bd \u03c4\u03bf \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03bf adapter layer, \u03bc\u03b5 \u03c4\u03b7\u03bd \u03c0\u03b1\u03c1\u03b1\u03b4\u03bf\u03c7\u03ae \u03cc\u03c4\u03b9 \u03c4\u03bf throughput \u03c4\u03bf\u03c5 \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03bf\u03c5 backbone \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03ba\u03bf\u03b9\u03bd\u03cc.<\/p>\n<div class=\"td-chart td-chart--metrics\" role=\"img\" aria-label=\"\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b1 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b1 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03b1 \u03c4\u03bf\u03c5 FrameFT \u03c3\u03b5 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 benchmark setups\">\n<div class=\"td-chart-head\">\n<p class=\"td-chart-title\">\u03a4\u03ad\u03c3\u03c3\u03b5\u03c1\u03b9\u03c2 \u03b1\u03c1\u03b9\u03b8\u03bc\u03bf\u03af \u03c0\u03bf\u03c5 \u03c3\u03c5\u03bd\u03bf\u03c8\u03af\u03b6\u03bf\u03c5\u03bd \u03c4\u03bf FrameFT<\/p>\n<p class=\"td-chart-subtitle\">\u039f\u03b9 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03c1\u03bf\u03ad\u03c1\u03c7\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b1\u03c0\u03cc \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03c4\u03b7\u03c2 \u03c0\u03c1\u03c9\u03c4\u03bf\u03b3\u03b5\u03bd\u03bf\u03cd\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b4\u03b5\u03bd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03bf\u03cd\u03bd \u03b5\u03bd\u03b9\u03b1\u03af\u03bf end-to-end production benchmark.<\/p>\n<\/div>\n<div class=\"td-chart-body\">\n<div class=\"td-metric-grid td-metric-grid--four-columns\">\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">24K<\/span><span class=\"td-metric-label\">Trainable parameters<\/span><span class=\"td-metric-note\">RoBERTa Base \u03c3\u03c4\u03bf GLUE<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">86,1<\/span><span class=\"td-metric-label\">\u039c\u03ad\u03c3\u03bf GLUE score<\/span><span class=\"td-metric-note\">\u0388\u03be\u03b9 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 RoBERTa Base<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">1,28 MB<\/span><span class=\"td-metric-label\">Adapter checkpoint<\/span><span class=\"td-metric-note\">Llama-2-7B \u03c3\u03b5 FP32<\/span><\/div>\n<div class=\"td-metric-card\"><span class=\"td-metric-value\">39,4K<\/span><span class=\"td-metric-label\">Tokens \u03b1\u03bd\u03ac \u03b4\u03b5\u03c5\u03c4\u03b5\u03c1\u03cc\u03bb\u03b5\u03c0\u03c4\u03bf<\/span><span class=\"td-metric-note\">Adapter layer, A100, Llama-2-7B<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<p>\u0391\u03c5\u03c4\u03cc \u03c4\u03bf \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03bf. \u0394\u03b5\u03bd \u03c0\u03c1\u03cc\u03ba\u03b5\u03b9\u03c4\u03b1\u03b9 \u03b3\u03b9\u03b1 \u03c0\u03bb\u03ae\u03c1\u03b5\u03c2 end-to-end benchmark \u03b5\u03bd\u03cc\u03c2 production API \u03bc\u03b5 batching, networking, retrieval \u03ba\u03b1\u03b9 moderation. \u0395\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c4\u03bc\u03ae\u03bc\u03b1\u03c4\u03bf\u03c2 \u03c0\u03bf\u03c5 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b9\u03c2 PEFT \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2. \u03a0\u03b1\u03c1\u03cc\u03bb\u03b1 \u03b1\u03c5\u03c4\u03ac, \u03b3\u03b9\u03b1 \u03c0\u03bb\u03b1\u03c4\u03c6\u03cc\u03c1\u03bc\u03b1 \u03c0\u03bf\u03c5 \u03ba\u03ac\u03bd\u03b5\u03b9 hot-swapping \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd adapters, \u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03c4\u03bf\u03c5 task-specific overhead \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ad\u03c7\u03b5\u03b9 \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1.<\/p>\n<p>\u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03cc\u03c4\u03b9 \u03c4\u03bf FrameFT \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03c4\u03b7\u03ba\u03b5 5% \u03ad\u03c9\u03c2 7% \u03b3\u03c1\u03b7\u03b3\u03bf\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 \u03b1\u03c0\u03cc \u03c4\u03bf LoRA \u03c3\u03c4\u03b1 \u03c0\u03ad\u03bd\u03c4\u03b5 instruction-tuning setups \u03ba\u03b1\u03b9 \u03b2\u03c1\u03ad\u03b8\u03b7\u03ba\u03b5 \u03b5\u03bd\u03c4\u03cc\u03c2 0,6 GiB \u03c4\u03b7\u03c2 peak GPU memory \u03c4\u03bf\u03c5. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03bf \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03bf\u03c2 backbone \u03b5\u03be\u03b1\u03ba\u03bf\u03bb\u03bf\u03c5\u03b8\u03b5\u03af \u03bd\u03b1 \u03ba\u03c5\u03c1\u03b9\u03b1\u03c1\u03c7\u03b5\u03af \u03c3\u03c4\u03b7 \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03ae \u03bc\u03bd\u03ae\u03bc\u03b7. \u03a4\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc checkpoint \u03b4\u03b5\u03bd \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03b3\u03c7\u03ad\u03b5\u03c4\u03b1\u03b9 \u03bc\u03b5 \u03b1\u03bd\u03c4\u03af\u03c3\u03c4\u03bf\u03b9\u03c7\u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf\u03c5 \u03c4\u03bf\u03c5 training footprint.<\/p>\n<h2 id=\"statherotita-convergence\">\u03a3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 convergence: \u03c4\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03b1\u03c0\u03bf\u03b4\u03b5\u03b9\u03ba\u03bd\u03cd\u03b5\u03c4\u03b1\u03b9<\/h2>\n<p>\u0397 \u03bc\u03b5\u03bb\u03ad\u03c4\u03b7 \u03b4\u03b5\u03bd \u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1. \u03a3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03c4\u03b7 \u03b3\u03b5\u03c9\u03bc\u03b5\u03c4\u03c1\u03af\u03b1 \u03c4\u03c9\u03bd Fusion Frames \u03bc\u03b5 \u03c4\u03b7 smoothness \u03c4\u03b7\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2. \u0391\u03bd \u03b7 \u03b1\u03c1\u03c7\u03b9\u03ba\u03ae \u03c3\u03c5\u03bd\u03ac\u03c1\u03c4\u03b7\u03c3\u03b7 \u03b1\u03c0\u03ce\u03bb\u03b5\u03b9\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 Lipschitz smooth \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac L, \u03c4\u03cc\u03c4\u03b5 \u03b7 reparameterized loss \u03c9\u03c2 \u03c0\u03c1\u03bf\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2 \u03ad\u03c7\u03b5\u03b9 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ac L \u03b5\u03c0\u03af \u03c4\u03b1 \u03ac\u03bd\u03c9 frame bounds \u03c4\u03c9\u03bd \u03c7\u03ce\u03c1\u03c9\u03bd \u03b5\u03b9\u03c3\u03cc\u03b4\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b5\u03be\u03cc\u03b4\u03bf\u03c5. \u0393\u03b9\u03b1 Parseval frames, \u03c4\u03b1 bounds \u03ba\u03b1\u03bd\u03bf\u03bd\u03b9\u03ba\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1.<\/p>\n<p>\u03a4\u03bf appendix \u03b4\u03af\u03bd\u03b5\u03b9 \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03c3\u03cd\u03b3\u03ba\u03bb\u03b9\u03c3\u03b7\u03c2 \u03c4\u03bf\u03c5 gradient descent \u03c0\u03c1\u03bf\u03c2 first-order stationary point, \u03c5\u03c0\u03cc \u03c4\u03bf\u03bd \u03b4\u03b7\u03bb\u03c9\u03bc\u03ad\u03bd\u03bf \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03cc \u03c3\u03c4\u03bf learning rate. \u0391\u03c5\u03c4\u03cc \u03b4\u03b5\u03bd \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf training \u03b8\u03b1 \u03b2\u03c1\u03b5\u03b9 \u03c4\u03bf \u03c0\u03b1\u03b3\u03ba\u03cc\u03c3\u03bc\u03b9\u03bf \u03b2\u03ad\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf \u03c3\u03b5 \u03b2\u03b1\u03b8\u03cd \u03bd\u03b5\u03c5\u03c1\u03c9\u03bd\u03b9\u03ba\u03cc \u03b4\u03af\u03ba\u03c4\u03c5\u03bf. \u03a3\u03b7\u03bc\u03b1\u03af\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03b7 \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c0\u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03bc\u03b5\u03c4\u03c1\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03b5\u03af \u03c7\u03c1\u03ae\u03c3\u03b9\u03bc\u03b7 \u03b9\u03b4\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 smoothness \u03ba\u03b1\u03b9 \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c0\u03b5\u03b9 \u03c4\u03b7\u03bd \u03c4\u03c5\u03c0\u03b9\u03ba\u03ae \u03b1\u03bd\u03ac\u03bb\u03c5\u03c3\u03b7 \u03c0\u03c1\u03bf\u03cc\u03b4\u03bf\u03c5 \u03b1\u03bd\u03ac \u03b2\u03ae\u03bc\u03b1.<\/p>\n<p>\u0393\u03b9\u03b1 \u03bc\u03b9\u03b1 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03bf\u03bc\u03ac\u03b4\u03b1, \u03c4\u03bf \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03bf\u03b9 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ad\u03c2 \u03c4\u03c9\u03bd frames \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c5\u03b8\u03b1\u03af\u03c1\u03b5\u03c4\u03b5\u03c2. \u0397 \u03c7\u03c1\u03ae\u03c3\u03b7 Tight\/Parseval Fusion Frames \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03c4\u03b1\u03b9 \u03ac\u03bc\u03b5\u03c3\u03b1 \u03bc\u03b5 \u03c4\u03bf bound \u03c4\u03b7\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03c3\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2. \u03a0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9, \u03cc\u03bc\u03c9\u03c2, \u03b1\u03bd\u03b1\u03b3\u03ba\u03b1\u03af\u03b1 \u03b7 \u03b5\u03bc\u03c0\u03b5\u03b9\u03c1\u03b9\u03ba\u03ae \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc training pipeline, \u03b3\u03b9\u03b1\u03c4\u03af \u03b7 \u03b8\u03b5\u03c9\u03c1\u03b7\u03c4\u03b9\u03ba\u03ae \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03b4\u03b5\u03bd \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03cc\u03bb\u03bf\u03c5\u03c2 \u03c4\u03bf\u03c5\u03c2 \u03c0\u03b1\u03c1\u03ac\u03b3\u03bf\u03bd\u03c4\u03b5\u03c2 \u03b5\u03bd\u03cc\u03c2 production stack.<\/p>\n<h2 id=\"epicheirimatiko-ai-stack\">\u03a0\u03bf\u03cd \u03c4\u03b1\u03b9\u03c1\u03b9\u03ac\u03b6\u03b5\u03b9 \u03c4\u03bf FrameFT \u03c3\u03b5 \u03ad\u03bd\u03b1 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc AI stack<\/h2>\n<p>\u0397 \u03c0\u03b9\u03bf \u03c6\u03c5\u03c3\u03b9\u03ba\u03ae \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ae \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 multi-tenant \u03c0\u03b5\u03c1\u03b9\u03b2\u03ac\u03bb\u03bb\u03bf\u03bd: \u03ad\u03bd\u03b1 \u03ba\u03bf\u03b9\u03bd\u03cc foundation model \u03ba\u03b1\u03b9 \u03c0\u03bf\u03bb\u03bb\u03bf\u03af \u03c0\u03b5\u03bb\u03ac\u03c4\u03b5\u03c2, brands \u03ae \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ad\u03c2 \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ad\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2. \u039c\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 checkpoints \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03b5\u03c5\u03ba\u03bf\u03bb\u03cc\u03c4\u03b5\u03c1\u03b7 \u03b1\u03c0\u03bf\u03b8\u03ae\u03ba\u03b5\u03c5\u03c3\u03b7 \u03ba\u03b1\u03b9 \u03b4\u03b9\u03b1\u03bd\u03bf\u03bc\u03ae. \u039a\u03bf\u03b9\u03bd\u03ad\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03c3\u03b7\u03bc\u03b1\u03af\u03bd\u03bf\u03c5\u03bd \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03bf duplicated state. \u039c\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf adapter throughput \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03ba\u03ac\u03bd\u03b5\u03b9 \u03c0\u03b9\u03bf \u03c0\u03c1\u03b1\u03ba\u03c4\u03b9\u03ba\u03ae \u03c4\u03b7 \u03b4\u03c5\u03bd\u03b1\u03bc\u03b9\u03ba\u03ae \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03c3\u03c9\u03c3\u03c4\u03ae\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03af\u03ba\u03b5\u03c5\u03c3\u03b7\u03c2 \u03b1\u03bd\u03ac request.<\/p>\n<p>\u0388\u03bd\u03b1 \u03b4\u03b5\u03cd\u03c4\u03b5\u03c1\u03bf \u03c3\u03b5\u03bd\u03ac\u03c1\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7 \u03c0\u03bf\u03bb\u03bb\u03ce\u03bd \u03c0\u03b5\u03b9\u03c1\u03b1\u03bc\u03ac\u03c4\u03c9\u03bd. Marketing, support \u03ba\u03b1\u03b9 e-commerce \u03bf\u03bc\u03ac\u03b4\u03b5\u03c2 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ac\u03b6\u03bf\u03c5\u03bd \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03b5\u03c4\u03b9\u03ba\u03ac datasets \u03ae policies. \u038c\u03c4\u03b1\u03bd \u03ba\u03ac\u03b8\u03b5 adapter \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c2, \u03b7 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7, \u03c4\u03bf rollback \u03ba\u03b1\u03b9 \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ce\u03bd \u03b3\u03af\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bf\u03c1\u03b3\u03b1\u03bd\u03c9\u03c4\u03b9\u03ba\u03ac \u03b5\u03c5\u03ba\u03bf\u03bb\u03cc\u03c4\u03b5\u03c1\u03b1. \u03a3\u03b5 \u03c4\u03ad\u03c4\u03bf\u03b9\u03b1 \u03ad\u03c1\u03b3\u03b1, \u03bf\u03b9 <a href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd \u03ba\u03b1\u03b9 AI<\/a> \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bc\u03b1\u03b6\u03af model registry, approvals, versioning \u03ba\u03b1\u03b9 \u03c0\u03b1\u03c1\u03b1\u03ba\u03bf\u03bb\u03bf\u03cd\u03b8\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03bf\u03cd \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03bf\u03c2.<\/p>\n<p>\u03a4\u03bf FrameFT \u03b4\u03b5\u03bd \u03bb\u03cd\u03bd\u03b5\u03b9 \u03b6\u03b7\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03c9\u03bd, privacy, licensing \u03ae evaluation. \u0388\u03bd\u03b1\u03c2 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03bf\u03c2 adapter \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03bc\u03ac\u03b8\u03b5\u03b9 \u03ba\u03b1\u03ba\u03ad\u03c2 \u03b5\u03c4\u03b9\u03ba\u03ad\u03c4\u03b5\u03c2 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03b5\u03c3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ac \u03bc\u03b5 \u03ad\u03bd\u03b1\u03bd \u03bc\u03b5\u03b3\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf. \u0397 governance \u03b4\u03b9\u03b1\u03b4\u03b9\u03ba\u03b1\u03c3\u03af\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 dataset lineage, tests \u03b1\u03bd\u03ac \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b7 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b1, monitoring \u03b3\u03b9\u03b1 drift \u03ba\u03b1\u03b9 \u03c3\u03b1\u03c6\u03ae \u03b4\u03c5\u03bd\u03b1\u03c4\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b1\u03c0\u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7\u03c2 \u03c0\u03c1\u03bf\u03b2\u03bb\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae\u03c2 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7\u03c2.<\/p>\n<div class=\"td-decision-band\">\n<p class=\"td-decision-kicker\">\u039a\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03bf \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae\u03c2<\/p>\n<p class=\"td-decision-title\"><strong>\u0391\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03c4\u03b5 \u03c4\u03bf FrameFT \u03cc\u03c4\u03b1\u03bd \u03c4\u03bf bottleneck \u03b5\u03af\u03bd\u03b1\u03b9 \u03bf \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc\u03c2 \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03bd\u03b1\u03bb\u03bb\u03b1\u03b3\u03ae \u03c4\u03c9\u03bd adapters<\/strong><\/p>\n<p>\u0391\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf adapter \u03ba\u03b1\u03b9 \u03b7 \u03ce\u03c1\u03b9\u03bc\u03b7 \u03c5\u03c0\u03bf\u03c3\u03c4\u03ae\u03c1\u03b9\u03be\u03b7 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03c9\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03c1\u03bf\u03c4\u03b5\u03c1\u03b1\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, \u03c4\u03bf LoRA \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b9\u03c3\u03c7\u03c5\u03c1\u03cc baseline. \u0391\u03bd \u03b4\u03b9\u03b1\u03c7\u03b5\u03b9\u03c1\u03af\u03b6\u03b5\u03c3\u03c4\u03b5 \u03b4\u03b5\u03ba\u03ac\u03b4\u03b5\u03c2 \u03ae \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf backbone, \u03c4\u03bf checkpoint size, \u03b7 load latency \u03ba\u03b1\u03b9 \u03c4\u03bf adapter throughput \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd \u03c3\u03c4\u03bf\u03c7\u03b5\u03c5\u03bc\u03ad\u03bd\u03bf FrameFT pilot.<\/p>\n<\/div>\n<h2 id=\"periorismoi-poc\">\u03a0\u03b5\u03c1\u03b9\u03bf\u03c1\u03b9\u03c3\u03bc\u03bf\u03af \u03ba\u03b1\u03b9 \u03b5\u03c1\u03c9\u03c4\u03ae\u03c3\u03b5\u03b9\u03c2 \u03c0\u03c1\u03b9\u03bd \u03b1\u03c0\u03cc \u03ad\u03bd\u03b1 proof of concept<\/h2>\n<p>\u0397 \u03b4\u03b7\u03bc\u03bf\u03c3\u03b9\u03b5\u03c5\u03bc\u03ad\u03bd\u03b7 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 preprint \u03c0\u03c1\u03ce\u03c4\u03b7\u03c2 \u03ad\u03ba\u03b4\u03bf\u03c3\u03b7\u03c2. \u039f\u03b9 \u03b4\u03bf\u03ba\u03b9\u03bc\u03ad\u03c2 \u03b5\u03ba\u03c4\u03b5\u03bb\u03ad\u03c3\u03c4\u03b7\u03ba\u03b1\u03bd \u03c3\u03b5 \u03b4\u03cd\u03bf NVIDIA A100 \u03bc\u03b5 40 GB \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03b7 \u03ba\u03b1\u03b8\u03b5\u03bc\u03af\u03b1, \u03b5\u03bd\u03ce \u03bf\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03c3\u03b5\u03b9\u03c2 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03bf\u03cd\u03bd \u03c3\u03c5\u03b3\u03ba\u03b5\u03ba\u03c1\u03b9\u03bc\u03ad\u03bd\u03b1 hyperparameters \u03ba\u03b1\u03b9 benchmarks. \u03a4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03bf\u03cd\u03bd \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ae \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7, \u03cc\u03c7\u03b9 \u03b5\u03b3\u03b3\u03cd\u03b7\u03c3\u03b7 \u03af\u03b4\u03b9\u03bf\u03c5 \u03b1\u03c0\u03bf\u03c4\u03b5\u03bb\u03ad\u03c3\u03bc\u03b1\u03c4\u03bf\u03c2 \u03c3\u03b5 \u03ba\u03ac\u03b8\u03b5 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1 \u03ae dataset.<\/p>\n<p>\u039f\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd \u03b5\u03c0\u03af\u03c3\u03b7\u03c2 \u03cc\u03c4\u03b9 \u03cc\u03c3\u03bf \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 \u03b4\u03b9\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c4\u03c9\u03bd \u03c5\u03c0\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03b7\u03bc\u03ac\u03c4\u03c9\u03bd, \u03bc\u03b5 \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03cc \u03c4\u03bf \u03c3\u03c5\u03bd\u03bf\u03bb\u03b9\u03ba\u03cc \u03b3\u03b9\u03bd\u03cc\u03bc\u03b5\u03bd\u03bf, \u03b1\u03c5\u03be\u03ac\u03bd\u03b5\u03c4\u03b1\u03b9 \u03b7 sparsity \u03c4\u03c9\u03bd Fusion Frames \u03b1\u03bb\u03bb\u03ac \u03bc\u03b5\u03b9\u03ce\u03bd\u03bf\u03bd\u03c4\u03b1\u03b9 \u03bf\u03b9 \u03b2\u03b1\u03b8\u03bc\u03bf\u03af \u03b5\u03bb\u03b5\u03c5\u03b8\u03b5\u03c1\u03af\u03b1\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03bd\u03b7\u03bc\u03ad\u03c1\u03c9\u03c3\u03b7\u03c2. \u0391\u03c5\u03c4\u03cc \u03bf\u03b4\u03b7\u03b3\u03b5\u03af \u03c3\u03b5 \u03bc\u03b9\u03ba\u03c1\u03ae \u03c0\u03c4\u03ce\u03c3\u03b7 \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7\u03c2 \u03c3\u03c4\u03b1 \u03c3\u03c7\u03b5\u03c4\u03b9\u03ba\u03ac \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1. \u03a5\u03c0\u03ac\u03c1\u03c7\u03b5\u03b9 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc trade-off \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b7\u03bd \u03c5\u03c0\u03bf\u03bb\u03bf\u03b3\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03bf\u03b9\u03ba\u03bf\u03bd\u03bf\u03bc\u03af\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b7\u03bd \u03b5\u03ba\u03c6\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1.<\/p>\n<p>\u0388\u03bd\u03b1 \u03c3\u03bf\u03b2\u03b1\u03c1\u03cc proof of concept \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 FrameFT \u03bc\u03b5 \u03c4\u03bf LoRA configuration \u03c0\u03bf\u03c5 \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03ae\u03b4\u03b7 \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1, \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf backbone, dataset, precision \u03ba\u03b1\u03b9 hardware. \u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03bf\u03c5\u03bb\u03ac\u03c7\u03b9\u03c3\u03c4\u03bf\u03bd task quality, training time, peak GPU memory, checkpoint size, load\/swap latency \u03ba\u03b1\u03b9 end-to-end serving latency. \u0391\u03bd \u03c4\u03bf use case \u03b5\u03af\u03bd\u03b1\u03b9 \u03c0\u03bf\u03bb\u03c5\u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03cc, \u03b7 \u03b5\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03b4\u03bf\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03bc\u03b5\u03c4\u03c1\u03b7\u03b8\u03b5\u03af \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac \u03b1\u03bd\u03c4\u03af \u03bd\u03b1 \u03c3\u03c5\u03bd\u03b1\u03c7\u03b8\u03b5\u03af \u03b1\u03c0\u03cc \u03b1\u03b3\u03b3\u03bb\u03b9\u03ba\u03ac benchmarks.<\/p>\n<p>\u0397 \u03b4\u03b7\u03bc\u03bf\u03c3\u03af\u03b5\u03c5\u03c3\u03b7 \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1 \u03b2\u03bf\u03b7\u03b8\u03ac, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03c5\u03c0\u03bf\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf\u03bd \u03ad\u03bb\u03b5\u03b3\u03c7\u03bf \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2. \u038c\u03c0\u03c9\u03c2 \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03ba\u03b1\u03b9 \u03b7 \u03b5\u03c5\u03c1\u03cd\u03c4\u03b5\u03c1\u03b7 \u03c3\u03c5\u03b6\u03ae\u03c4\u03b7\u03c3\u03b7 \u03b3\u03b9\u03b1 \u03c4\u03b7\u03bd <a href=\"https:\/\/twodots.gr\/neuro-symbolic-ai-anaparagogimotita-erevnas\/\">\u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c3\u03c4\u03b7\u03bd \u03ad\u03c1\u03b5\u03c5\u03bd\u03b1 AI<\/a>, \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03ba\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03b5\u03b9 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03b2\u03b9\u03b2\u03bb\u03b9\u03bf\u03b8\u03b7\u03ba\u03ce\u03bd, seeds, training recipe \u03ba\u03b1\u03b9 evaluation harness \u03c0\u03c1\u03b9\u03bd \u03c3\u03c5\u03b3\u03ba\u03c1\u03af\u03bd\u03b5\u03b9 \u03bc\u03b5\u03b8\u03cc\u03b4\u03bf\u03c5\u03c2. \u03a0\u03b1\u03c1\u03ac\u03bb\u03bb\u03b7\u03bb\u03b1, \u03c4\u03bf \u03b5\u03bd\u03b5\u03c1\u03b3\u03b5\u03b9\u03b1\u03ba\u03cc \u03ba\u03b1\u03b9 \u03c5\u03c0\u03bf\u03b4\u03bf\u03bc\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03c4\u03c1\u03b7\u03c3\u03b7 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf hardware, \u03cc\u03c7\u03b9 \u03c5\u03c0\u03cc\u03b8\u03b5\u03c3\u03b7 \u03b1\u03c0\u03cc \u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03c9\u03bd\u00b7 \u03b1\u03c5\u03c4\u03cc \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf \u03b5\u03af\u03b4\u03bf\u03c2 \u03c0\u03b5\u03b9\u03b8\u03b1\u03c1\u03c7\u03af\u03b1\u03c2 \u03c0\u03bf\u03c5 \u03b1\u03c0\u03b1\u03b9\u03c4\u03b5\u03af \u03b7 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c4\u03bf\u03c5 <a href=\"https:\/\/twodots.gr\/edge-ai-energeia-akriveia-kostizei-perissotero\/\">trade-off \u03b1\u03ba\u03c1\u03af\u03b2\u03b5\u03b9\u03b1\u03c2 \u03ba\u03b1\u03b9 \u03b1\u03c0\u03bf\u03b4\u03bf\u03c4\u03b9\u03ba\u03cc\u03c4\u03b7\u03c4\u03b1\u03c2 \u03c3\u03c4\u03bf Edge AI<\/a>.<\/p>\n<h2 id=\"vhmata-frameft-pilot\">\u0395\u03c0\u03c4\u03ac \u03b2\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b3\u03b9\u03b1 \u03ad\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 FrameFT pilot<\/h2>\n<p>\u039f \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03c4\u03bf\u03c5 pilot \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03bd\u03b1 \u03b5\u03c0\u03b9\u03b2\u03b5\u03b2\u03b1\u03b9\u03ce\u03c3\u03b5\u03b9 \u03c4\u03bf paper, \u03b1\u03bb\u03bb\u03ac \u03bd\u03b1 \u03b1\u03c0\u03b1\u03bd\u03c4\u03ae\u03c3\u03b5\u03b9 \u03b1\u03bd \u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 \u03b2\u03b5\u03bb\u03c4\u03b9\u03ce\u03bd\u03b5\u03b9 \u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1. \u0397 \u03b1\u03be\u03b9\u03bf\u03bb\u03cc\u03b3\u03b7\u03c3\u03b7 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03cc baseline, \u03af\u03b4\u03b9\u03b1 \u03b4\u03b5\u03b4\u03bf\u03bc\u03ad\u03bd\u03b1, \u03bb\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b9\u03ba\u03ad\u03c2 \u03bc\u03b5\u03c4\u03c1\u03ae\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03c1\u03b7\u03c4\u03cc \u03ba\u03c1\u03b9\u03c4\u03ae\u03c1\u03b9\u03bf \u03b1\u03c0\u03bf\u03b4\u03bf\u03c7\u03ae\u03c2.<\/p>\n<div class=\"td-step-list\">\n<p class=\"td-step-list-title\">\u03a0\u03bb\u03ac\u03bd\u03bf proof of concept \u03b3\u03b9\u03b1 FrameFT<\/p>\n<ol>\n<li><span class=\"td-step-kicker\">Use case<\/span><strong>\u039f\u03c1\u03af\u03c3\u03c4\u03b5 \u03c4\u03b7 \u03bc\u03bf\u03bd\u03ac\u03b4\u03b1 \u03b5\u03be\u03b5\u03b9\u03b4\u03af\u03ba\u03b5\u03c5\u03c3\u03b7\u03c2<\/strong>\n<p>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03b1\u03bd \u03ba\u03ac\u03b8\u03b5 adapter \u03b1\u03bd\u03c4\u03b9\u03c3\u03c4\u03bf\u03b9\u03c7\u03b5\u03af \u03c3\u03b5 \u03c0\u03b5\u03bb\u03ac\u03c4\u03b7, \u03b3\u03bb\u03ce\u03c3\u03c3\u03b1, brand, policy \u03ae task \u03ba\u03b1\u03b9 \u03c0\u03cc\u03c3\u03b5\u03c2 \u03b5\u03bd\u03b5\u03c1\u03b3\u03ad\u03c2 \u03b5\u03ba\u03b4\u03cc\u03c3\u03b5\u03b9\u03c2 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03b7\u03c1\u03af\u03b6\u03b5\u03b9 \u03c4\u03bf \u03c3\u03cd\u03c3\u03c4\u03b7\u03bc\u03b1.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Baseline<\/span><strong>\u039a\u03bb\u03b5\u03b9\u03b4\u03ce\u03c3\u03c4\u03b5 \u03c4\u03bf \u03c3\u03b7\u03bc\u03b5\u03c1\u03b9\u03bd\u03cc LoRA configuration<\/strong>\n<p>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03af\u03b4\u03b9\u03bf backbone, target modules, dataset split, precision, seed policy \u03ba\u03b1\u03b9 evaluation harness \u03ce\u03c3\u03c4\u03b5 \u03b7 \u03c3\u03cd\u03b3\u03ba\u03c1\u03b9\u03c3\u03b7 \u03bd\u03b1 \u03b1\u03c0\u03bf\u03bc\u03bf\u03bd\u03ce\u03bd\u03b5\u03b9 \u03c4\u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf PEFT.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Quality<\/span><strong>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b9\u03c2 \u03ba\u03c1\u03af\u03c3\u03b9\u03bc\u03b5\u03c2 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03c7\u03c9\u03c1\u03b9\u03c3\u03c4\u03ac<\/strong>\n<p>\u039c\u03b7\u03bd \u03b1\u03c1\u03ba\u03b5\u03c3\u03c4\u03b5\u03af\u03c4\u03b5 \u03c3\u03b5 \u03ad\u03bd\u03b1\u03bd \u03bc\u03ad\u03c3\u03bf \u03cc\u03c1\u03bf. \u0395\u03bb\u03ad\u03b3\u03be\u03c4\u03b5 \u03b5\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac, \u03c3\u03c0\u03ac\u03bd\u03b9\u03b5\u03c2 \u03c0\u03c1\u03bf\u03b8\u03ad\u03c3\u03b5\u03b9\u03c2, safety failures \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b7\u03b3\u03bf\u03c1\u03af\u03b5\u03c2 \u03bc\u03b5 \u03c5\u03c8\u03b7\u03bb\u03cc \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Training<\/span><strong>\u039a\u03b1\u03c4\u03b1\u03b3\u03c1\u03ac\u03c8\u03c4\u03b5 \u03c7\u03c1\u03cc\u03bd\u03bf \u03ba\u03b1\u03b9 peak GPU memory<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 wall-clock \u03c7\u03c1\u03cc\u03bd\u03bf, \u03b1\u03c0\u03bf\u03c4\u03c5\u03c7\u03b7\u03bc\u03ad\u03bd\u03b1 runs, convergence \u03ba\u03b1\u03b9 \u03ba\u03b1\u03c4\u03b1\u03bd\u03ac\u03bb\u03c9\u03c3\u03b7 \u03bc\u03bd\u03ae\u03bc\u03b7\u03c2 \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 hardware \u03bc\u03b5 \u03c4\u03bf \u03af\u03b4\u03b9\u03bf monitoring.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Serving<\/span><strong>\u0394\u03bf\u03ba\u03b9\u03bc\u03ac\u03c3\u03c4\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc adapter switching<\/strong>\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03c3\u03c4\u03b5 \u03bc\u03ad\u03b3\u03b5\u03b8\u03bf\u03c2 checkpoint, load time, hot-swap latency, concurrency \u03ba\u03b1\u03b9 end-to-end latency \u03bc\u03b1\u03b6\u03af \u03bc\u03b5 batching, retrieval \u03ba\u03b1\u03b9 moderation.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Governance<\/span><strong>\u03a3\u03c5\u03bd\u03b4\u03ad\u03c3\u03c4\u03b5 \u03ba\u03ac\u03b8\u03b5 adapter \u03bc\u03b5 lineage \u03ba\u03b1\u03b9 approval<\/strong>\n<p>\u0391\u03c0\u03bf\u03b8\u03b7\u03ba\u03b5\u03cd\u03c3\u03c4\u03b5 dataset version, \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1, metrics, \u03b9\u03b4\u03b9\u03bf\u03ba\u03c4\u03ae\u03c4\u03b7, \u03b7\u03bc\u03b5\u03c1\u03bf\u03bc\u03b7\u03bd\u03af\u03b1 \u03bb\u03ae\u03be\u03b7\u03c2 \u03ba\u03b1\u03b9 rollback target \u03c0\u03c1\u03b9\u03bd \u03b5\u03c0\u03b9\u03c4\u03c1\u03ad\u03c8\u03b5\u03c4\u03b5 \u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03b9\u03ba\u03ae \u03b5\u03bd\u03b5\u03c1\u03b3\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7.<\/p>\n<\/li>\n<li><span class=\"td-step-kicker\">Decision<\/span><strong>\u03a7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03ae\u03c3\u03c4\u03b5 \u03c0\u03c1\u03bf\u03ba\u03b1\u03b8\u03bf\u03c1\u03b9\u03c3\u03bc\u03ad\u03bd\u03bf acceptance gate<\/strong>\n<p>\u03a5\u03b9\u03bf\u03b8\u03b5\u03c4\u03ae\u03c3\u03c4\u03b5 \u03c4\u03b7 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf \u03bc\u03cc\u03bd\u03bf \u03b1\u03bd \u03b7 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b5\u03bd\u03c4\u03cc\u03c2 \u03bf\u03c1\u03af\u03bf\u03c5 \u03ba\u03b1\u03b9 \u03b7 \u03bc\u03b5\u03af\u03c9\u03c3\u03b7 \u03c3\u03b5 storage \u03ae swap latency \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03c1\u03ba\u03b5\u03c4\u03ae \u03b3\u03b9\u03b1 \u03bd\u03b1 \u03b4\u03b9\u03ba\u03b1\u03b9\u03bf\u03bb\u03bf\u03b3\u03ae\u03c3\u03b5\u03b9 \u03c4\u03bf \u03bd\u03ad\u03bf operational path.<\/p>\n<\/li>\n<\/ol>\n<\/div>\n<div class=\"td-article-note\"><strong>\u039c\u03b7\u03bd \u03bc\u03b5\u03c4\u03c1\u03ac\u03c4\u03b5 \u03bc\u03cc\u03bd\u03bf \u03c4\u03bf adapter layer:<\/strong> \u03ad\u03bd\u03b1 production API \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 backbone, tokenizer, batching, retrieval, network, guardrails \u03ba\u03b1\u03b9 observability. \u0397 \u03c4\u03b5\u03bb\u03b9\u03ba\u03ae \u03b1\u03c0\u03cc\u03c6\u03b1\u03c3\u03b7 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b2\u03b1\u03c3\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03b5 end-to-end latency \u03ba\u03b1\u03b9 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b1\u03bd\u03ac \u03b5\u03c0\u03b9\u03c4\u03c5\u03c7\u03b7\u03bc\u03ad\u03bd\u03bf request.<\/div>\n<h2 id=\"symperasma\">\u03a4\u03bf \u03bf\u03c5\u03c3\u03b9\u03b1\u03c3\u03c4\u03b9\u03ba\u03cc \u03c3\u03c5\u03bc\u03c0\u03ad\u03c1\u03b1\u03c3\u03bc\u03b1 \u03b3\u03b9\u03b1 decision-makers<\/h2>\n<p>\u03a4\u03bf FrameFT \u03b4\u03b5\u03af\u03c7\u03bd\u03b5\u03b9 \u03cc\u03c4\u03b9 \u03c4\u03bf parameter-efficient fine-tuning \u03b4\u03b5\u03bd \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bd\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bf\u03c1\u03af\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03b7 low-rank \u03c0\u03b1\u03c1\u03b1\u03b3\u03bf\u03bd\u03c4\u03bf\u03c0\u03bf\u03af\u03b7\u03c3\u03b7. \u039c\u03b9\u03b1 \u03c0\u03c1\u03bf\u03c3\u03b5\u03ba\u03c4\u03b9\u03ba\u03ac \u03b4\u03bf\u03bc\u03b7\u03bc\u03ad\u03bd\u03b7, \u03b1\u03c1\u03b1\u03b9\u03ae \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03ac\u03c3\u03c4\u03b1\u03c3\u03b7 \u03c3\u03b5 \u03ba\u03bf\u03b9\u03bd\u03ac \u03c5\u03c0\u03bf\u03b4\u03b9\u03b1\u03c3\u03c4\u03ae\u03bc\u03b1\u03c4\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b4\u03b9\u03b1\u03c4\u03b7\u03c1\u03ae\u03c3\u03b5\u03b9 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03ae \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1 \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c5\u03c4\u03cc\u03c7\u03c1\u03bf\u03bd\u03b1 \u03bd\u03b1 \u03bc\u03b5\u03b9\u03ce\u03c3\u03b5\u03b9 \u03b4\u03c1\u03b1\u03c3\u03c4\u03b9\u03ba\u03ac \u03c4\u03bf task-specific state. \u0391\u03c5\u03c4\u03cc \u03ad\u03c7\u03b5\u03b9 \u03b9\u03b4\u03b9\u03b1\u03af\u03c4\u03b5\u03c1\u03b7 \u03c3\u03b7\u03bc\u03b1\u03c3\u03af\u03b1 \u03cc\u03c4\u03b1\u03bd \u03bf \u03c3\u03c4\u03cc\u03c7\u03bf\u03c2 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03bc\u03cc\u03bd\u03bf \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03ba\u03ac\u03b4\u03b5\u03c2 \u03ae \u03c7\u03b9\u03bb\u03b9\u03ac\u03b4\u03b5\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03c5\u03bc\u03ad\u03bd\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bb\u03bb\u03b1\u03b3\u03ad\u03c2 \u03c0\u03ac\u03bd\u03c9 \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf backbone.<\/p>\n<p>\u0397 \u03c3\u03c9\u03c3\u03c4\u03ae \u03b1\u03bd\u03ac\u03b3\u03bd\u03c9\u03c3\u03b7 \u03b4\u03b5\u03bd \u03b5\u03af\u03bd\u03b1\u03b9 \u00ab\u03c4\u03bf FrameFT \u03b1\u03bd\u03c4\u03b9\u03ba\u03b1\u03b8\u03b9\u03c3\u03c4\u03ac \u03c4\u03bf LoRA\u00bb. \u0395\u03af\u03bd\u03b1\u03b9 \u03cc\u03c4\u03b9 \u03c0\u03c1\u03bf\u03c3\u03b8\u03ad\u03c4\u03b5\u03b9 \u03c4\u03b5\u03c7\u03bd\u03b9\u03ba\u03ac \u03c4\u03b5\u03ba\u03bc\u03b7\u03c1\u03b9\u03c9\u03bc\u03ad\u03bd\u03b7 \u03b5\u03c0\u03b9\u03bb\u03bf\u03b3\u03ae \u03c3\u03c4\u03bf evaluation matrix. \u0391\u03bd \u03c4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03b2\u03c1\u03af\u03c3\u03ba\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c4\u03bf storage, \u03c3\u03c4\u03bf hot-swapping \u03ae \u03c3\u03c4\u03bf\u03bd \u03b1\u03c1\u03b9\u03b8\u03bc\u03cc \u03c4\u03c9\u03bd adapters, \u03c4\u03b1 \u03b5\u03c5\u03c1\u03ae\u03bc\u03b1\u03c4\u03b1 \u03b1\u03be\u03af\u03b6\u03bf\u03c5\u03bd \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b1\u03b3\u03c9\u03b3\u03ae. \u0391\u03bd \u03b7 \u03bf\u03bc\u03ac\u03b4\u03b1 \u03c7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03bc\u03ad\u03b3\u03b9\u03c3\u03c4\u03b7 \u03c9\u03c1\u03b9\u03bc\u03cc\u03c4\u03b7\u03c4\u03b1 \u03b5\u03c1\u03b3\u03b1\u03bb\u03b5\u03af\u03c9\u03bd \u03ba\u03b1\u03b9 \u03b5\u03c5\u03c1\u03b5\u03af\u03b1 \u03c5\u03c0\u03bf\u03c3\u03c4\u03ae\u03c1\u03b9\u03be\u03b7, \u03c4\u03bf LoRA \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03c4\u03bf \u03ba\u03b1\u03b8\u03b9\u03b5\u03c1\u03c9\u03bc\u03ad\u03bd\u03bf \u03c3\u03b7\u03bc\u03b5\u03af\u03bf \u03b1\u03bd\u03b1\u03c6\u03bf\u03c1\u03ac\u03c2. \u0397 \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03b1\u03be\u03af\u03b1 \u03b8\u03b1 \u03c0\u03c1\u03bf\u03ba\u03cd\u03c8\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03b7 \u03bc\u03b5\u03c4\u03c1\u03b7\u03bc\u03ad\u03bd\u03b7 \u03b4\u03b9\u03b1\u03c6\u03bf\u03c1\u03ac \u03c3\u03c4\u03bf \u03b4\u03b9\u03ba\u03cc \u03c3\u03b1\u03c2 workload.<\/p>\n<section class=\"td-service-cta\">\n<div class=\"td-service-cta-content\">\n<p class=\"td-service-cta-eyebrow\">\u0391\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03af \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03ae\u03c3\u03b5\u03c9\u03bd &amp; AI<\/p>\n<p class=\"td-service-cta-title\"><strong>\u03a3\u03c7\u03b5\u03b4\u03b9\u03ac\u03c3\u03c4\u03b5 \u03ad\u03bd\u03b1 AI workflow \u03c0\u03bf\u03c5 \u03bc\u03b5\u03c4\u03c1\u03ac \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, \u03ba\u03cc\u03c3\u03c4\u03bf\u03c2 \u03ba\u03b1\u03b9 rollback<\/strong><\/p>\n<p>\u0397 TWO DOTS \u03c3\u03c5\u03bd\u03b4\u03ad\u03b5\u03b9 \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1, datasets, approvals, validators \u03ba\u03b1\u03b9 monitoring \u03c3\u03b5 \u03b5\u03bb\u03b5\u03b3\u03c7\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03c1\u03bf\u03ad\u03c2 \u03b3\u03b9\u03b1 e-commerce, CRM, customer support \u03ba\u03b1\u03b9 \u03b5\u03c3\u03c9\u03c4\u03b5\u03c1\u03b9\u03ba\u03ad\u03c2 \u03b5\u03c6\u03b1\u03c1\u03bc\u03bf\u03b3\u03ad\u03c2.<\/p>\n<div class=\"td-service-cta-actions\"><a class=\"td-service-cta-button\" href=\"https:\/\/twodots.gr\/aftomatismoi-epicheiriseon-ai\/\">\u0394\u03b5\u03af\u03c4\u03b5 \u03c4\u03b9\u03c2 \u03bb\u03cd\u03c3\u03b5\u03b9\u03c2 \u03b1\u03c5\u03c4\u03bf\u03bc\u03b1\u03c4\u03b9\u03c3\u03bc\u03bf\u03cd &amp; AI<\/a><\/div>\n<\/div>\n<\/section>\n<section id=\"sychnes-erotiseis\" class=\"td-faq-section\">\n<div class=\"td-faq\">\n<p class=\"td-faq-heading\">\u010cesto postavljana pitanja<\/p>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf FrameFT;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0395\u03af\u03bd\u03b1\u03b9 \u03bc\u03ad\u03b8\u03bf\u03b4\u03bf\u03c2 parameter-efficient fine-tuning \u03c0\u03bf\u03c5 \u03b1\u03bd\u03b1\u03c0\u03b1\u03c1\u03b9\u03c3\u03c4\u03ac \u03c4\u03b9\u03c2 \u03b5\u03bd\u03b7\u03bc\u03b5\u03c1\u03ce\u03c3\u03b5\u03b9\u03c2 \u03b2\u03b1\u03c1\u03ce\u03bd \u03bc\u03b5 \u03b1\u03c1\u03b1\u03b9\u03bf\u03cd\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2 \u03bc\u03ad\u03c3\u03b1 \u03c3\u03b5 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 Fusion Frames, \u03b5\u03bd\u03ce \u03ba\u03c1\u03b1\u03c4\u03ac \u03c0\u03b1\u03b3\u03c9\u03bc\u03ad\u03bd\u03bf \u03c4\u03bf \u03b1\u03c1\u03c7\u03b9\u03ba\u03cc \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03ce\u03c2 \u03b4\u03b9\u03b1\u03c6\u03ad\u03c1\u03b5\u03b9 \u03b1\u03c0\u03cc \u03c4\u03bf LoRA;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf LoRA \u03bc\u03b1\u03b8\u03b1\u03af\u03bd\u03b5\u03b9 \u03b4\u03cd\u03bf \u03c0\u03c5\u03ba\u03bd\u03bf\u03cd\u03c2 low-rank \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2. \u03a4\u03bf FrameFT \u03ba\u03c1\u03b1\u03c4\u03ac \u03c3\u03c4\u03b1\u03b8\u03b5\u03c1\u03ad\u03c2, \u03b1\u03bb\u03b3\u03bf\u03c1\u03b9\u03b8\u03bc\u03b9\u03ba\u03ac \u03c0\u03b1\u03c1\u03b1\u03b3\u03cc\u03bc\u03b5\u03bd\u03b5\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03ba\u03b1\u03b9 \u03b5\u03ba\u03c0\u03b1\u03b9\u03b4\u03b5\u03cd\u03b5\u03b9 \u03b1\u03c1\u03b1\u03b9\u03bf\u03cd\u03c2 coefficient matrices \u03c0\u03bf\u03c5 \u03bc\u03c0\u03bf\u03c1\u03bf\u03cd\u03bd \u03bd\u03b1 \u03bc\u03bf\u03b9\u03c1\u03ac\u03b6\u03bf\u03bd\u03c4\u03b1\u03b9 \u03c4\u03b7 \u03b4\u03bf\u03bc\u03ae \u03c4\u03bf\u03c5\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03b5 layers.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u0395\u03af\u03bd\u03b1\u03b9 \u03c0\u03ac\u03bd\u03c4\u03b1 \u03ba\u03b1\u03bb\u03cd\u03c4\u03b5\u03c1\u03bf \u03b1\u03c0\u03cc \u03c4\u03bf LoRA;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u03a3\u03c4\u03bf\u03c5\u03c2 \u03c0\u03af\u03bd\u03b1\u03ba\u03b5\u03c2 \u03c4\u03b7\u03c2 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03b1\u03bd\u03c4\u03b1\u03b3\u03c9\u03bd\u03b9\u03c3\u03c4\u03b9\u03ba\u03cc \u03ba\u03b1\u03b9 \u03c3\u03c5\u03c7\u03bd\u03ac \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03c0\u03bf\u03bb\u03cd \u03bb\u03b9\u03b3\u03cc\u03c4\u03b5\u03c1\u03b5\u03c2 \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03c4\u03c1\u03bf\u03c5\u03c2, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03ba\u03b5\u03c1\u03b4\u03af\u03b6\u03b5\u03b9 \u03ba\u03ac\u03b8\u03b5 \u03b5\u03c0\u03b9\u03bc\u03ad\u03c1\u03bf\u03c5\u03c2 task \u03ae \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03bf. \u03a4\u03bf LoRA \u03c0\u03b1\u03c1\u03b1\u03bc\u03ad\u03bd\u03b5\u03b9 \u03b1\u03c0\u03b1\u03c1\u03b1\u03af\u03c4\u03b7\u03c4\u03bf baseline.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03cc\u03c3\u03bf \u03bc\u03b9\u03ba\u03c1\u03cc \u03ae\u03c4\u03b1\u03bd \u03c4\u03bf \u03b1\u03bd\u03b1\u03c6\u03b5\u03c1\u03cc\u03bc\u03b5\u03bd\u03bf checkpoint;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u0393\u03b9\u03b1 Llama-2-7B \u03bf\u03b9 \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03b5\u03af\u03c2 \u03b1\u03bd\u03b1\u03c6\u03ad\u03c1\u03bf\u03c5\u03bd 1,28 MB \u03b3\u03b9\u03b1 FrameFT \u03ad\u03bd\u03b1\u03bd\u03c4\u03b9 67,1 MB \u03b3\u03b9\u03b1 LoRA rank-32, \u03bc\u03b5 \u03c4\u03b1 \u03b4\u03cd\u03bf checkpoints \u03c3\u03b5 FP32.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039c\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 \u03c0\u03c1\u03b1\u03b3\u03bc\u03b1\u03c4\u03b9\u03ba\u03ae \u03c4\u03b1\u03c7\u03cd\u03c4\u03b7\u03c4\u03b1 inference;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u039c\u03b5\u03c4\u03c1\u03ae\u03b8\u03b7\u03ba\u03b5 throughput \u03c4\u03bf\u03c5 \u03c0\u03c1\u03cc\u03c3\u03b8\u03b5\u03c4\u03bf\u03c5 adapter layer \u03c3\u03b5 NVIDIA A100, \u03cc\u03c7\u03b9 \u03bf\u03bb\u03cc\u03ba\u03bb\u03b7\u03c1\u03bf\u03c5 production API. \u03a4\u03bf end-to-end \u03b1\u03c0\u03bf\u03c4\u03ad\u03bb\u03b5\u03c3\u03bc\u03b1 \u03bc\u03c0\u03bf\u03c1\u03b5\u03af \u03bd\u03b1 \u03b1\u03bb\u03bb\u03ac\u03b6\u03b5\u03b9 \u03bb\u03cc\u03b3\u03c9 backbone, batching, retrieval, networking \u03ba\u03b1\u03b9 guardrails.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u039b\u03b5\u03b9\u03c4\u03bf\u03c5\u03c1\u03b3\u03b5\u03af \u03bc\u03cc\u03bd\u03bf \u03c3\u03b5 \u03b3\u03bb\u03c9\u03c3\u03c3\u03b9\u03ba\u03ac \u03bc\u03bf\u03bd\u03c4\u03ad\u03bb\u03b1;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u038c\u03c7\u03b9. \u0397 \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b1 \u03c0\u03b5\u03c1\u03b9\u03bb\u03b1\u03bc\u03b2\u03ac\u03bd\u03b5\u03b9 \u03c0\u03b5\u03b9\u03c1\u03ac\u03bc\u03b1\u03c4\u03b1 \u03c3\u03b5 Vision Transformers \u03b3\u03b9\u03b1 \u03bf\u03ba\u03c4\u03ce \u03b5\u03c1\u03b3\u03b1\u03c3\u03af\u03b5\u03c2 \u03c4\u03b1\u03be\u03b9\u03bd\u03cc\u03bc\u03b7\u03c3\u03b7\u03c2 \u03b5\u03b9\u03ba\u03cc\u03bd\u03c9\u03bd, \u03b1\u03bb\u03bb\u03ac \u03b4\u03b5\u03bd \u03b1\u03be\u03b9\u03bf\u03bb\u03bf\u03b3\u03b5\u03af detection, segmentation \u03ae multimodal generation.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a0\u03bf\u03b9\u03bf \u03b5\u03af\u03bd\u03b1\u03b9 \u03c4\u03bf \u03b2\u03b1\u03c3\u03b9\u03ba\u03cc \u03b5\u03c0\u03b9\u03c7\u03b5\u03b9\u03c1\u03b7\u03bc\u03b1\u03c4\u03b9\u03ba\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a4\u03bf \u03c0\u03b9\u03b8\u03b1\u03bd\u03cc \u03cc\u03c6\u03b5\u03bb\u03bf\u03c2 \u03b5\u03af\u03bd\u03b1\u03b9 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 task-specific checkpoints, \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b1\u03bd\u03ac\u03bc\u03b5\u03c3\u03b1 \u03c3\u03c4\u03b1 layers \u03ba\u03b1\u03b9 \u03c4\u03b1\u03c7\u03cd\u03c4\u03b5\u03c1\u03bf hot-swapping \u03cc\u03c4\u03b1\u03bd \u03ad\u03bd\u03b1 \u03ba\u03bf\u03b9\u03bd\u03cc backbone \u03b5\u03be\u03c5\u03c0\u03b7\u03c1\u03b5\u03c4\u03b5\u03af \u03c0\u03bf\u03bb\u03bb\u03ad\u03c2 \u03b5\u03be\u03b5\u03b9\u03b4\u03b9\u03ba\u03b5\u03cd\u03c3\u03b5\u03b9\u03c2.<\/p>\n<\/div>\n<\/details>\n<details class=\"td-faq-item\">\n<summary class=\"td-faq-title\">\u03a4\u03b9 \u03c0\u03c1\u03ad\u03c0\u03b5\u03b9 \u03bd\u03b1 \u03b5\u03bb\u03ad\u03b3\u03be\u03b5\u03b9 \u03bc\u03b9\u03b1 \u03b5\u03c4\u03b1\u03b9\u03c1\u03b5\u03af\u03b1 \u03c0\u03c1\u03b9\u03bd \u03c4\u03bf \u03c5\u03b9\u03bf\u03b8\u03b5\u03c4\u03ae\u03c3\u03b5\u03b9;<\/summary>\n<div class=\"td-faq-content\">\n<p>\u03a7\u03c1\u03b5\u03b9\u03ac\u03b6\u03b5\u03c4\u03b1\u03b9 \u03c3\u03c5\u03b3\u03ba\u03c1\u03b9\u03c4\u03b9\u03ba\u03cc proof of concept \u03c3\u03c4\u03bf \u03af\u03b4\u03b9\u03bf backbone, dataset, precision \u03ba\u03b1\u03b9 hardware, \u03bc\u03b5 metrics \u03b3\u03b9\u03b1 \u03c0\u03bf\u03b9\u03cc\u03c4\u03b7\u03c4\u03b1, training time, peak GPU memory, checkpoint size, load latency \u03ba\u03b1\u03b9 end-to-end serving latency.<\/p>\n<\/div>\n<\/details>\n<\/div>\n<\/section>\n<div class=\"td-source-list\">\n<p id=\"piges\" class=\"td-source-list-title\">Izvori<\/p>\n<ul>\n<li><a href=\"https:\/\/arxiv.org\/html\/2608.26430\" target=\"_blank\" rel=\"noopener\">Adepu, Zhang, Kumar, Singh, Fine-Tuning of Transformer models with Frames, arXiv:2608.26430<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/vsingh-group\/FrameFT\" target=\"_blank\" rel=\"noopener\">FrameFT, \u03b5\u03c0\u03af\u03c3\u03b7\u03bc\u03bf \u03b1\u03c0\u03bf\u03b8\u03b5\u03c4\u03ae\u03c1\u03b9\u03bf \u03ba\u03ce\u03b4\u03b9\u03ba\u03b1 \u03c4\u03c9\u03bd \u03c3\u03c5\u03b3\u03b3\u03c1\u03b1\u03c6\u03ad\u03c9\u03bd<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/2106.09685\" target=\"_blank\" rel=\"noopener\">Hu et al., LoRA: Low-Rank Adaptation of Large Language Models<\/a><\/li>\n<li><a href=\"https:\/\/www.nist.gov\/itl\/ai-risk-management-framework\" target=\"_blank\" rel=\"noopener\">NIST AI Risk Management Framework, \u03c0\u03bb\u03b1\u03af\u03c3\u03b9\u03bf \u03b3\u03b9\u03b1 \u03c4\u03b7 \u03b4\u03b9\u03b1\u03c7\u03b5\u03af\u03c1\u03b9\u03c3\u03b7 \u03ba\u03b9\u03bd\u03b4\u03cd\u03bd\u03c9\u03bd AI<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>\u03a4\u03bf FrameFT \u03c7\u03c1\u03b7\u03c3\u03b9\u03bc\u03bf\u03c0\u03bf\u03b9\u03b5\u03af \u03b1\u03c1\u03b1\u03b9\u03bf\u03cd\u03c2 \u03c3\u03c5\u03bd\u03c4\u03b5\u03bb\u03b5\u03c3\u03c4\u03ad\u03c2 \u03ba\u03b1\u03b9 \u03ba\u03bf\u03b9\u03bd\u03ad\u03c2 \u03b2\u03ac\u03c3\u03b5\u03b9\u03c2 \u03b3\u03b9\u03b1 \u03bc\u03b9\u03ba\u03c1\u03cc\u03c4\u03b5\u03c1\u03b1 AI adapters. \u03a4\u03b9 \u03ad\u03b4\u03b5\u03b9\u03be\u03b1\u03bd \u03c4\u03b1 benchmarks \u03ba\u03b1\u03b9 \u03c0\u03ce\u03c2 \u03c3\u03c4\u03ae\u03bd\u03b5\u03c4\u03b1\u03b9 \u03ad\u03bd\u03b1 \u03b1\u03c3\u03c6\u03b1\u03bb\u03ad\u03c2 pilot.<\/p>","protected":false},"author":1,"featured_media":113763,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","footnotes":""},"categories":[199],"tags":[21222,17781,21816,19275,20561],"class_list":["post-105099","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-techniti-noimosyni","tag-ai-ypodomi","tag-fine-tuning","tag-frameft","tag-lora","tag-transformers"],"_links":{"self":[{"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/posts\/105099","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/comments?post=105099"}],"version-history":[{"count":0,"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/posts\/105099\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/media\/113763"}],"wp:attachment":[{"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/media?parent=105099"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/categories?post=105099"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/twodots.gr\/hr\/wp-json\/wp\/v2\/tags?post=105099"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}