{"id":29460,"date":"2025-09-13T16:00:28","date_gmt":"2025-09-13T13:00:28","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/"},"modified":"2025-09-13T16:00:28","modified_gmt":"2025-09-13T13:00:28","slug":"buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/","title":{"rendered":"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?"},"content":{"rendered":"<p>B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/p>\n<p>B\u00fcy\u00fck dil modelleri (BDM) son y\u0131llarda do\u011fal dil i\u015fleme alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7t\u0131, ancak bu modellerin boyutu ve hesaplama maliyeti, kaynaklar\u0131 s\u0131n\u0131rl\u0131 olan kullan\u0131c\u0131lar i\u00e7in bir engel olu\u015fturuyor. \u0130\u015fte bu noktada d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon teknikleri devreye giriyor. Bu teknikler, b\u00fcy\u00fck bir BDM&#8217;nin performans\u0131n\u0131 az miktarda ek bilgiyle \u00f6zelle\u015ftirmeyi ama\u00e7l\u0131yor.  Bu makalede, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon tekniklerini ad\u0131m ad\u0131m a\u00e7\u0131klayarak, farkl\u0131 y\u00f6ntemleri kar\u015f\u0131la\u015ft\u0131rarak ve ger\u00e7ek d\u00fcnya \u00f6rnekleriyle peki\u015ftirece\u011fiz.<\/p>\n<h2>B\u00fcy\u00fck Dil Modelleri ve Adaptasyonun Temelleri: Neden \u0130htiya\u00e7 Duyuyoruz?<\/h2>\n<p>B\u00fcy\u00fck dil modelleri, devasa miktarda veri \u00fczerinde e\u011fitilmi\u015f karma\u015f\u0131k sinir a\u011flar\u0131d\u0131r.  Bu modeller, metin olu\u015fturma, \u00e7eviri, soru cevaplama gibi bir\u00e7ok g\u00f6revde etkileyici sonu\u00e7lar \u00fcretmektedir. Ancak, bu modellerin boyutu ve e\u011fitim maliyeti olduk\u00e7a y\u00fcksektir.  Her yeni g\u00f6rev veya alan i\u00e7in s\u0131f\u0131rdan yeni bir model e\u011fitmek hem pahal\u0131 hem de zaman al\u0131c\u0131d\u0131r.  Bu nedenle, \u00f6nceden e\u011fitilmi\u015f bir BDM&#8217;yi yeni verilere uyarlayarak performans\u0131n\u0131 art\u0131rmak i\u00e7in adaptasyon tekniklerine ihtiya\u00e7 duyulur. D\u00fc\u015f\u00fck r\u00fctbeli adaptasyon, bu ihtiyac\u0131 kar\u015f\u0131lamak i\u00e7in tasarlanm\u0131\u015f verimli bir y\u00f6ntemdir.  Bu y\u00f6ntem, modelin t\u00fcm parametrelerini yeniden e\u011fitmek yerine, sadece k\u00fc\u00e7\u00fck bir alt k\u00fcmesini g\u00fcncelleyerek hesaplama maliyetini ve depolama alan\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131r.  Ba\u015fka bir deyi\u015fle, mevcut modelin g\u00fcc\u00fcnden faydalanarak, daha az kayna\u011fa sahip sistemlerin de g\u00fc\u00e7l\u00fc performans g\u00f6stermesini sa\u011flar.<\/p>\n<h2>D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Teknikleri: Hangi Y\u00f6ntemleri Kullanabiliriz?<\/h2>\n<p>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyon, temelde modelin parametre uzay\u0131nda d\u00fc\u015f\u00fck r\u00fctbeli bir g\u00fcncelleme yaparak \u00e7al\u0131\u015f\u0131r.  Bu, modelin t\u00fcm parametrelerini de\u011fi\u015ftirmek yerine, sadece d\u00fc\u015f\u00fck boyutlu bir alt uzayda de\u011fi\u015fiklikler yap\u0131lmas\u0131 anlam\u0131na gelir.  Birka\u00e7 pop\u00fcler d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon tekni\u011fi \u015funlard\u0131r:<\/p>\n<ul>\n<li><b>Parameter-Efficient Fine-Tuning (PEFT):<\/b> Bu teknik, model parametrelerinin sadece k\u00fc\u00e7\u00fck bir k\u0131sm\u0131n\u0131 g\u00fcncelleyerek hesaplama maliyetini d\u00fc\u015f\u00fcrmeyi hedefler.  LoRA (Low-Rank Adaptation), Adapter ve Prompt Tuning gibi y\u00f6ntemler PEFT \u00e7at\u0131s\u0131 alt\u0131nda yer al\u0131r.<\/li>\n<li><b>LoRA (Low-Rank Adaptation):<\/b> LoRA, her katmanda d\u00fc\u015f\u00fck r\u00fctbeli bir matris ekleyerek modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 g\u00fcncellemektedir.  Bu, modelin orijinal a\u011f\u0131rl\u0131klar\u0131n\u0131 de\u011fi\u015ftirmeden performans\u0131n\u0131 art\u0131rmaya olanak tan\u0131r ve depolama ihtiyac\u0131n\u0131 azalt\u0131r.  LoRA, \u00f6zellikle b\u00fcy\u00fck modellerde olduk\u00e7a etkilidir.<\/li>\n<li><b>Adapter Methods:<\/b> Adapter y\u00f6ntemleri, modelin belirli katmanlar\u0131na ek mod\u00fcller ekleyerek \u00e7al\u0131\u015f\u0131r. Bu mod\u00fcller, yeni veriler \u00fczerinde e\u011fitim al\u0131r ve modelin \u00e7\u0131k\u0131\u015f\u0131n\u0131 etkiler.  Adapterlerin ana avantaj\u0131, modelin geri kalan\u0131n\u0131 de\u011fi\u015ftirmeden yeni bir g\u00f6rev i\u00e7in \u00f6zelle\u015ftirilmelerini sa\u011flamas\u0131d\u0131r.<\/li>\n<li><b>Prompt Tuning:<\/b> Prompt tuning, modelin girdi k\u0131sm\u0131na eklenen k\u00fc\u00e7\u00fck boyutlu vekt\u00f6rleri ince ayarlayarak \u00e7al\u0131\u015f\u0131r. Bu y\u00f6ntem, modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 de\u011fi\u015ftirmeden yeni g\u00f6revler i\u00e7in uyarlanabilirli\u011fini art\u0131r\u0131r ve hesaplama maliyetini d\u00fc\u015f\u00fcr\u00fcr.<\/li>\n<\/ul>\n<p>Bu teknikler aras\u0131nda LoRA, \u00f6zellikle pop\u00fclaritesini h\u0131zla art\u0131ran bir y\u00f6ntemdir.  \u00c7\u00fcnk\u00fc hem etkili hem de uygulanmas\u0131 nispeten kolayd\u0131r.  Di\u011fer y\u00f6ntemler ise farkl\u0131 avantajlar sunmaktad\u0131r. \u00d6rne\u011fin, Adapter y\u00f6ntemleri, modelin farkl\u0131 g\u00f6revler i\u00e7in farkl\u0131 adapt\u00f6rlerle \u00f6zelle\u015ftirilmesini sa\u011flayarak esneklik sunar.<\/p>\n<h2>D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyonun Ad\u0131m Ad\u0131m Uygulamas\u0131: Bir LoRA \u00d6rne\u011fi<\/h2>\n<p>Bu b\u00f6l\u00fcmde, LoRA y\u00f6ntemini kullanarak bir BDM&#8217;yi d\u00fc\u015f\u00fck r\u00fctbeli olarak nas\u0131l uyarlayabilece\u011finizi ad\u0131m ad\u0131m g\u00f6sterece\u011fiz.  Bu \u00f6rnek, kavramsal bir a\u00e7\u0131klama olup, belirli bir k\u00fct\u00fcphaneye veya \u00e7er\u00e7eveye ba\u011fl\u0131 de\u011fildir.  Ger\u00e7ek bir uygulama i\u00e7in uygun bir derin \u00f6\u011frenme k\u00fct\u00fcphanesi (\u00f6rne\u011fin, Hugging Face Transformers) kullanman\u0131z gerekecektir.<\/p>\n<ol>\n<li><b>\u00d6nceden E\u011fitilmi\u015f Model Se\u00e7imi:<\/b>  \u0130lk ad\u0131m, belirli bir g\u00f6rev i\u00e7in uygun bir \u00f6nceden e\u011fitilmi\u015f BDM se\u00e7mektir.<\/li>\n<li><b>LoRA Katmanlar\u0131n\u0131n Eklenmesi:<\/b>  Sonraki ad\u0131m, BDM&#8217;nin belirli katmanlar\u0131na d\u00fc\u015f\u00fck r\u00fctbeli matrisler (\u00f6rne\u011fin, A ve B matrisleri) eklemektir.  Bu matrislerin boyutlar\u0131, r\u00fctbe parametresi ile belirlenir.  R\u00fctbe de\u011feri ne kadar d\u00fc\u015f\u00fckse, adaptasyon o kadar d\u00fc\u015f\u00fck r\u00fctbeli olur ve bu da daha az parametre g\u00fcncellemesi gerekti\u011fi anlam\u0131na gelir.<\/li>\n<li><b>\u0130nce Ayar (Fine-tuning):<\/b>  Yeni verilere g\u00f6re sadece eklenen d\u00fc\u015f\u00fck r\u00fctbeli matrisler A ve B  e\u011fitilir.  Modelin orijinal a\u011f\u0131rl\u0131klar\u0131 dondurulur (yani g\u00fcncellenmez).<\/li>\n<li><b>De\u011ferlendirme:<\/b>  E\u011fitimden sonra, modelin performans\u0131 yeni veriler \u00fczerinde de\u011ferlendirilir.<\/li>\n<\/ol>\n<p>Kod \u00f6rne\u011fi (kurgusal):<\/p>\n<pre><code class=\"language-python\"># Bu kod sadece kavramsal bir \u00f6rnektir. Ger\u00e7ek bir uygulama i\u00e7in bir derin \u00f6\u011frenme k\u00fct\u00fcphanesi gereklidir.\nmodel = load_pretrained_model(\"bert-base-uncased\") # \u00d6nceden e\u011fitilmi\u015f modeli y\u00fckler\nlora_layers = add_lora_layers(model, rank=4) # R\u00fctbe 4 ile LoRA katmanlar\u0131 ekler\ntrain_lora_layers(lora_layers, training_data) # Sadece LoRA katmanlar\u0131n\u0131 e\u011fitir\nevaluate_model(model, test_data) # Modeli de\u011ferlendirir<\/pre>\n<p><\/code><\/p>\n<h2>D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Y\u00f6ntemlerinin Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131: Hangisi Daha Uygun?<\/h2>\n<p>Farkl\u0131 d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon y\u00f6ntemlerinin performans\u0131, g\u00f6rev ve veri k\u00fcmesine ba\u011fl\u0131 olarak de\u011fi\u015fir.  Genel olarak, LoRA, adaptasyonun h\u0131z\u0131 ve kolayl\u0131\u011f\u0131 a\u00e7\u0131s\u0131ndan iyi bir denge sunar. Adapter y\u00f6ntemleri, farkl\u0131 g\u00f6revler i\u00e7in modelin esnek bir \u015fekilde \u00f6zelle\u015ftirilmesini sa\u011flar. Prompt tuning ise, modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 de\u011fi\u015ftirmeden adaptasyon yapabilmesi nedeniyle avantajl\u0131d\u0131r.  A\u015fa\u011f\u0131daki tablo, bu y\u00f6ntemlerin baz\u0131 \u00f6zelliklerini kar\u015f\u0131la\u015ft\u0131rmaktad\u0131r:<\/p>\n<table>\n<tr>\n<th>Y\u00f6ntem<\/th>\n<th>Parametre Say\u0131s\u0131<\/th>\n<th>Uygulama Karma\u015f\u0131kl\u0131\u011f\u0131<\/th>\n<th>Esneklik<\/th>\n<th>Performans<\/th>\n<\/tr>\n<tr>\n<td>LoRA<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>Orta<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>\u0130yi<\/td>\n<\/tr>\n<tr>\n<td>Adapter<\/td>\n<td>Orta<\/td>\n<td>Orta-Y\u00fcksek<\/td>\n<td>Y\u00fcksek<\/td>\n<td>\u0130yi<\/td>\n<\/tr>\n<tr>\n<td>Prompt Tuning<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>Orta<\/td>\n<\/tr>\n<\/table>\n<p>Uygun y\u00f6ntemin se\u00e7imi, belirli bir uygulama i\u00e7in gerekli performans, kaynak k\u0131s\u0131tlamalar\u0131 ve esneklik gereksinimlerine ba\u011fl\u0131d\u0131r. Daha fazla bilgi i\u00e7in (https:\/\/fatihsoysal.com) inceleyebilirsiniz.<\/p>\n<h2>Ger\u00e7ek D\u00fcnya Senaryolar\u0131: Vaka Analizleri<\/h2>\n<p>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyon, \u00e7e\u015fitli ger\u00e7ek d\u00fcnya senaryolar\u0131nda ba\u015far\u0131yla uygulanm\u0131\u015ft\u0131r.  \u00d6rne\u011fin, bir m\u00fc\u015fteri hizmetleri sohbet robotunu belirli bir \u00fcr\u00fcn yelpazesi i\u00e7in \u00f6zelle\u015ftirmek i\u00e7in, \u00f6nceden e\u011fitilmi\u015f bir BDM LoRA veya Adapter y\u00f6ntemiyle d\u00fc\u015f\u00fck r\u00fctbeli olarak uyarlanabilir.  Bu, modelin daha do\u011fru ve alakal\u0131 yan\u0131tlar \u00fcretmesini sa\u011flar.  Benzer \u015fekilde, t\u0131p alan\u0131nda, s\u0131n\u0131rl\u0131 t\u0131bbi verilerle e\u011fitilmi\u015f bir BDM, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon ile daha iyi performans g\u00f6sterebilir.  Ba\u015fka bir \u00f6rnek olarak,  bir dil modelini belirli bir dil leh\u00e7esine uyarlamak i\u00e7in de d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon kullan\u0131labilir.  Bu, modelin bu leh\u00e7eyi daha iyi anlamas\u0131n\u0131 ve kullanmas\u0131n\u0131 sa\u011flar.  Bu \u00f6rnekler, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyonun \u00e7e\u015fitli alanlarda maliyet ve zaman tasarrufu sa\u011flayarak b\u00fcy\u00fck modellerin daha geni\u015f bir uygulama yelpazesine yay\u0131lmas\u0131na katk\u0131da bulundu\u011funu g\u00f6stermektedir.<\/p>\n<h2>\u0130leri D\u00fczey Teknikler ve \u0130pu\u00e7lar\u0131<\/h2>\n<p>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyonun performans\u0131n\u0131 iyile\u015ftirmek i\u00e7in \u00e7e\u015fitli ileri d\u00fczey teknikler kullan\u0131labilir.  \u00d6rne\u011fin, farkl\u0131 r\u00fctbe de\u011ferlerinin performansa etkisi incelenebilir.  Ayn\u0131 zamanda, farkl\u0131 katmanlarda farkl\u0131 r\u00fctbe de\u011ferleri kullanarak daha ince ayar yap\u0131labilir.  Ayr\u0131ca, farkl\u0131 optimizasyon algoritmalar\u0131 ve \u00f6\u011frenme oranlar\u0131 deneyilebilir.  Hiperparametre optimizasyonu, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyonun performans\u0131n\u0131 art\u0131rmak i\u00e7in \u00e7ok \u00f6nemlidir.   \u00d6rne\u011fin, LoRA'da kullan\u0131lan r\u00fctbe de\u011feri, adaptasyonun ba\u015far\u0131s\u0131 i\u00e7in kritik bir hiperparametredir.  \u00c7ok d\u00fc\u015f\u00fck bir r\u00fctbe, modelin performans\u0131n\u0131 yeterince iyile\u015ftirmeyebilirken, \u00e7ok y\u00fcksek bir r\u00fctbe, hesaplama maliyetini art\u0131rabilir.  Bu nedenle, en uygun r\u00fctbe de\u011ferini bulmak i\u00e7in deneysel \u00e7al\u0131\u015fmalara ihtiya\u00e7 vard\u0131r.  Bunun yan\u0131nda,  farkl\u0131 \u00f6\u011frenme oranlar\u0131 ve optimizasyon algoritmalar\u0131 denemek de performans\u0131 art\u0131rmaya yard\u0131mc\u0131 olabilir.  Daha fazla bilgi i\u00e7in uzmanlar\u0131n makalelerini ve kaynaklar\u0131n\u0131 inceleyebilirsiniz.<\/p>\n<h2>Sonu\u00e7<\/h2>\n<p>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyon, b\u00fcy\u00fck dil modellerinin kaynaklar\u0131 s\u0131n\u0131rl\u0131 ortamlarda kullan\u0131lmas\u0131n\u0131 sa\u011flayan g\u00fc\u00e7l\u00fc bir tekniktir.  LoRA, Adapter ve Prompt Tuning gibi y\u00f6ntemler, \u00f6nceden e\u011fitilmi\u015f modellerin yeni g\u00f6revlere h\u0131zl\u0131 ve verimli bir \u015fekilde uyarlanmas\u0131n\u0131 sa\u011flar.  Bu tekniklerin se\u00e7imi, uygulamaya \u00f6zg\u00fc ihtiya\u00e7lara ba\u011fl\u0131d\u0131r.  Ancak, genel olarak d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon, b\u00fcy\u00fck dil modellerinin daha yayg\u0131n ve eri\u015filebilir hale gelmesinde \u00f6nemli bir rol oynamaktad\u0131r.  Bu teknikler hakk\u0131nda daha fazla bilgi edinmek ve farkl\u0131 uygulamalar\u0131 ke\u015ffetmek i\u00e7in daha fazla ara\u015ft\u0131rma yapmak faydal\u0131 olacakt\u0131r.  Daha fazla bilgi i\u00e7in (https:\/\/fatihsoysal.com) inceleyebilirsiniz.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<ul>\n<li><b>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyon, b\u00fcy\u00fck modellerin performans\u0131n\u0131 ne kadar iyile\u015ftirebilir?<\/b> \u0130yile\u015ftirme, kullan\u0131lan y\u00f6nteme, veri k\u00fcmesine ve modelin kendisine ba\u011fl\u0131 olarak de\u011fi\u015fir ancak \u00f6nemli \u00f6l\u00e7\u00fcde performans art\u0131\u015f\u0131 sa\u011flanabilir.<\/li>\n<li><b>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyonun hesaplama maliyeti nedir?<\/b> Geleneksel ince ayar y\u00f6ntemlerine g\u00f6re \u00e7ok daha d\u00fc\u015f\u00fck bir hesaplama maliyeti s\u00f6z konusudur.<\/li>\n<li><b>Hangi durumlarda d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon kullan\u0131lmal\u0131d\u0131r?<\/b> Kaynaklar s\u0131n\u0131rl\u0131ysa, h\u0131zl\u0131 adaptasyon gerekliyse veya modelin orijinal a\u011f\u0131rl\u0131klar\u0131n\u0131 korumak isteniyorsa d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon tercih edilebilir.<\/li>\n<li><b>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyonun dezavantajlar\u0131 nelerdir?<\/b> Baz\u0131 durumlarda, geleneksel ince ayardan daha d\u00fc\u015f\u00fck performans sergileyebilir. Ayr\u0131ca, do\u011fru hiperparametrelerin se\u00e7imi performans i\u00e7in \u00f6nemlidir.<\/li>\n<li><b>D\u00fc\u015f\u00fck r\u00fctbeli adaptasyonu hangi k\u00fct\u00fcphaneler destekliyor?<\/b> Hugging Face Transformers, PyTorch gibi pop\u00fcler derin \u00f6\u011frenme k\u00fct\u00fcphaneleri, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon y\u00f6ntemlerini desteklemektedir.<\/li>\n<\/ul>\n<p>Yazar: Fatih Soysal<\/p>\n","protected":false},"excerpt":{"rendered":"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r? B\u00fcy\u00fck dil modelleri (BDM) son y\u0131llarda do\u011fal dil i\u015fleme alan\u0131nda&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-29460","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck dil modelleri (BDM) son y\u0131llarda do\u011fal dil i\u015fleme alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7t\u0131, ancak bu modellerin boyutu ve hesaplama maliyeti, kaynaklar\u0131 s\u0131n\u0131rl\u0131 olan kullan\u0131c\u0131lar i\u00e7in bir engel olu\u015fturuyor. \u0130\u015fte bu noktada d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon teknikleri devreye giriyor. Bu teknikler, b\u00fcy\u00fck bir BDM&#039;nin performans\u0131n\u0131 az miktarda ek bilgiyle \u00f6zelle\u015ftirmeyi ama\u00e7l\u0131yor. Bu makalede, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon tekniklerini ad\u0131m ad\u0131m a\u00e7\u0131klayarak, farkl\u0131 y\u00f6ntemleri kar\u015f\u0131la\u015ft\u0131rarak ve ger\u00e7ek d\u00fcnya \u00f6rnekleriyle peki\u015ftirece\u011fiz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?\" \/>\n<meta property=\"og:description\" content=\"B\u00fcy\u00fck dil modelleri (BDM) son y\u0131llarda do\u011fal dil i\u015fleme alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7t\u0131, ancak bu modellerin boyutu ve hesaplama maliyeti, kaynaklar\u0131 s\u0131n\u0131rl\u0131 olan kullan\u0131c\u0131lar i\u00e7in bir engel olu\u015fturuyor. \u0130\u015fte bu noktada d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon teknikleri devreye giriyor. Bu teknikler, b\u00fcy\u00fck bir BDM&#039;nin performans\u0131n\u0131 az miktarda ek bilgiyle \u00f6zelle\u015ftirmeyi ama\u00e7l\u0131yor. Bu makalede, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon tekniklerini ad\u0131m ad\u0131m a\u00e7\u0131klayarak, farkl\u0131 y\u00f6ntemleri kar\u015f\u0131la\u015ft\u0131rarak ve ger\u00e7ek d\u00fcnya \u00f6rnekleriyle peki\u015ftirece\u011fiz.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-09-13T13:00:28+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?\",\"datePublished\":\"2025-09-13T13:00:28+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/\"},\"wordCount\":1608,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/\",\"name\":\"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-09-13T13:00:28+00:00\",\"description\":\"B\u00fcy\u00fck dil modelleri (BDM) son y\u0131llarda do\u011fal dil i\u015fleme alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7t\u0131, ancak bu modellerin boyutu ve hesaplama maliyeti, kaynaklar\u0131 s\u0131n\u0131rl\u0131 olan kullan\u0131c\u0131lar i\u00e7in bir engel olu\u015fturuyor. \u0130\u015fte bu noktada d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon teknikleri devreye giriyor. 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Bu makalede, d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon tekniklerini ad\u0131m ad\u0131m a\u00e7\u0131klayarak, farkl\u0131 y\u00f6ntemleri kar\u015f\u0131la\u015ft\u0131rarak ve ger\u00e7ek d\u00fcnya \u00f6rnekleriyle peki\u015ftirece\u011fiz.","og_url":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/","og_site_name":"Kodlar\u0131n Gizemli D\u00fcnyas\u0131","article_published_time":"2025-09-13T13:00:28+00:00","author":"Fatih Soysal","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Fatih Soysal","Tahmini okuma s\u00fcresi":"8 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/#article","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/"},"author":{"name":"Fatih Soysal","@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"headline":"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?","datePublished":"2025-09-13T13:00:28+00:00","mainEntityOfPage":{"@id":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/"},"wordCount":1608,"commentCount":0,"publisher":{"@id":"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1"},"inLanguage":"tr","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/#respond"]}],"copyrightYear":"2025","copyrightHolder":{"@id":"https:\/\/fatihsoysal.com\/blog\/#organization"}},{"@type":"WebPage","@id":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/","url":"https:\/\/fatihsoysal.com\/blog\/buyuk-dil-modellerinde-dusuk-rutbeli-adaptasyon-nasil-calisir\/","name":"B\u00fcy\u00fck Dil Modellerinde D\u00fc\u015f\u00fck R\u00fctbeli Adaptasyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?","isPartOf":{"@id":"https:\/\/fatihsoysal.com\/blog\/#website"},"datePublished":"2025-09-13T13:00:28+00:00","description":"B\u00fcy\u00fck dil modelleri (BDM) son y\u0131llarda do\u011fal dil i\u015fleme alan\u0131nda \u00e7\u0131\u011f\u0131r a\u00e7t\u0131, ancak bu modellerin boyutu ve hesaplama maliyeti, kaynaklar\u0131 s\u0131n\u0131rl\u0131 olan kullan\u0131c\u0131lar i\u00e7in bir engel olu\u015fturuyor. \u0130\u015fte bu noktada d\u00fc\u015f\u00fck r\u00fctbeli adaptasyon teknikleri devreye giriyor. Bu teknikler, b\u00fcy\u00fck bir BDM'nin performans\u0131n\u0131 az miktarda ek bilgiyle \u00f6zelle\u015ftirmeyi ama\u00e7l\u0131yor. 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