Qwen3.5 Vision-Language Modellerine Ba\u015flang\u0131\u00e7 Rehberi<\/title><br \/>\n<body><\/p>\n<p>G\u00f6rsel ve metinsel verileri ayn\u0131 anda i\u015fleyebilen yapay zeka modelleri, g\u00fcn\u00fcm\u00fcz teknolojisinde \u00e7\u0131\u011f\u0131r a\u00e7\u0131yor. Peki, bu karma\u015f\u0131k verileri anlamland\u0131rmak ve bunlara anlaml\u0131 yan\u0131tlar \u00fcretmek i\u00e7in hangi ara\u00e7lar\u0131 kullanabiliriz? \u0130\u015fte tam bu noktada, Qwen3.5 Vision-Language Modelleri (VLM) devreye giriyor. Bu rehber, Qwen3.5’in sundu\u011fu yetenekleri s\u0131f\u0131rdan ke\u015ffetmenizi, kurulumdan ileri d\u00fczey uygulamalara kadar her ad\u0131m\u0131 kolayca anlaman\u0131z\u0131 sa\u011flayacak. E\u011fer siz de yapay zekan\u0131n g\u00f6rsel ve dil yeteneklerini birle\u015ftiren g\u00fcc\u00fcn\u00fc projelerinize ta\u015f\u0131mak istiyorsan\u0131z, do\u011fru yerdesiniz. Gelin, Qwen3.5 ile gelece\u011fin \u00e7ok modlu uygulamalar\u0131na ilk ad\u0131m\u0131 birlikte atal\u0131m.<\/p>\n<h2>\u00c7ok Modlu Yapay Zeka ile Gelece\u011fe Ad\u0131m Atmak: Neden Qwen3.5?<\/h2>\n<p>G\u00fcn\u00fcm\u00fcz dijital d\u00fcnyas\u0131nda, sadece metinleri veya sadece g\u00f6rselleri anlayan yapay zeka modelleri art\u0131k yeterli gelmeyebiliyor. \u0130nsanlar olarak bizler, \u00e7evremizdeki d\u00fcnyay\u0131 hem g\u00f6rsel hem de i\u015fitsel duyular\u0131m\u0131zla alg\u0131lar, bu bilgileri birle\u015ftirerek anlaml\u0131 kararlar veririz. Yapay zekan\u0131n da benzer bir yetene\u011fe sahip olmas\u0131, bir\u00e7ok sekt\u00f6rde devrim niteli\u011finde yeniliklerin kap\u0131s\u0131n\u0131 aral\u0131yor. \u0130\u015fte bu noktada, <strong>\u00e7ok modlu yapay zeka<\/strong> modelleri, \u00f6zellikle de <strong>Vision-Language Models (VLM)<\/strong>, \u00f6n plana \u00e7\u0131k\u0131yor.<\/p>\n<p>Geleneksel yapay zeka modelleri genellikle tek bir veri t\u00fcr\u00fcne odaklan\u0131r: ya metin i\u015fleme (do\u011fal dil i\u015fleme – NLP) ya da g\u00f6r\u00fcnt\u00fc i\u015fleme (bilgisayar g\u00f6r\u00fc\u015f\u00fc). Ancak, ger\u00e7ek d\u00fcnya senaryolar\u0131 genellikle her iki veri t\u00fcr\u00fcn\u00fc de ayn\u0131 anda i\u00e7erir. \u00d6rne\u011fin, bir e-ticaret sitesinde bir \u00fcr\u00fcn\u00fcn foto\u011fraf\u0131na bak\u0131p a\u00e7\u0131klamalar\u0131n\u0131 okuyarak karar veririz; bir g\u00fcvenlik kameras\u0131 g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fc izlerken ayn\u0131 zamanda olaylar\u0131 sesli komutlarla yorumlamak isteyebiliriz. Bu t\u00fcr karma\u015f\u0131k g\u00f6revler i\u00e7in, hem g\u00f6rselleri hem de metinleri ayn\u0131 anda anlay\u0131p i\u015fleyebilen modellere ihtiya\u00e7 duyar\u0131z. Qwen3.5, bu ihtiyac\u0131 kar\u015f\u0131lamak \u00fczere tasarlanm\u0131\u015f g\u00fc\u00e7l\u00fc ve yetenekli bir VLM’dir.<\/p>\n<p>Qwen3.5, Alibaba Cloud taraf\u0131ndan geli\u015ftirilen Qwen serisinin en yeni \u00fcyelerinden biridir. Bu model, \u00f6zellikle b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde e\u011fitilmi\u015f olmas\u0131 sayesinde, hem y\u00fcksek kaliteli g\u00f6r\u00fcnt\u00fc anlama hem de do\u011fal dil i\u015fleme yeteneklerini bir araya getiriyor. Bu entegre yakla\u015f\u0131m, Qwen3.5’i \u00e7e\u015fitli uygulamalar i\u00e7in ideal bir ara\u00e7 haline getiriyor. \u00d6rne\u011fin, bir g\u00f6rseldeki nesneleri tan\u0131yabilir, bu nesneler aras\u0131ndaki ili\u015fkileri anlayabilir, hatta g\u00f6rselle ilgili karma\u015f\u0131k sorular\u0131 yan\u0131tlayabilir veya g\u00f6rsele uygun detayl\u0131 a\u00e7\u0131klamalar \u00fcretebilir. Bu yetenekler, sa\u011fl\u0131k sekt\u00f6r\u00fcnden perakendeye, e\u011fitimden otomasyona kadar geni\u015f bir yelpazede yenilik\u00e7i \u00e7\u00f6z\u00fcmler sunma potansiyeli ta\u015f\u0131r.<\/p>\n<p>Qwen3.5’i tercih etmenin temel nedenlerinden biri, a\u00e7\u0131k kaynak toplulu\u011funa olan katk\u0131s\u0131 ve eri\u015filebilirli\u011fidir. Bu sayede geli\u015ftiriciler, modelin g\u00fcc\u00fcnden kolayca faydalanabilir ve kendi projelerine entegre edebilirler. Ayr\u0131ca, s\u00fcrekli geli\u015ftirilen bir model olmas\u0131, gelecekteki g\u00fcncellemeler ve iyile\u015ftirmelerle daha da g\u00fc\u00e7l\u00fc hale gelece\u011fi anlam\u0131na gelir. Bu makalede, Qwen3.5’in temel prensiplerinden ba\u015flayarak, nas\u0131l kurulaca\u011f\u0131n\u0131, basit uygulamalar\u0131n nas\u0131l geli\u015ftirilece\u011fini ve hatta ileri d\u00fczey kullan\u0131m senaryolar\u0131n\u0131 ad\u0131m ad\u0131m inceleyece\u011fiz. Amac\u0131m\u0131z, okuyucunun konuya tamamen yabanc\u0131 olsa bile, makalenin sonunda Qwen3.5 ile kendi projelerini hayata ge\u00e7irebilecek bilgi birikimine sahip olmas\u0131n\u0131 sa\u011flamakt\u0131r.<\/p>\n<h2>Qwen3.5 Vision-Language Modelleri Nedir ve Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Qwen3.5 Vision-Language Model (VLM), ad\u0131ndan da anla\u015f\u0131laca\u011f\u0131 gibi, “g\u00f6rsel” ve “dil” yeteneklerini bir araya getiren bir yapay zeka modelidir. Temel olarak, bu model hem g\u00f6r\u00fcnt\u00fcleri “g\u00f6rebilir” hem de metinleri “okuyabilir” ve bu iki farkl\u0131 bilgi t\u00fcr\u00fcn\u00fc ayn\u0131 anda i\u015fleyerek anlaml\u0131 \u00e7\u0131kt\u0131lar \u00fcretebilir. Geleneksel olarak, bir yapay zeka modeli ya sadece bir g\u00f6r\u00fcnt\u00fcy\u00fc analiz eder (\u00f6rne\u011fin, bir resimdeki kediyi tan\u0131r) ya da sadece bir metni i\u015fler (\u00f6rne\u011fin, bir c\u00fcmlenin anlam\u0131n\u0131 \u00e7\u0131kar\u0131r). Ancak Qwen3.5 gibi VLM’ler, bu s\u0131n\u0131rlar\u0131 ortadan kald\u0131rarak \u00e7ok daha karma\u015f\u0131k ve insan benzeri bir anlay\u0131\u015f seviyesine ula\u015f\u0131r.<\/p>\n<p>Peki, bu karma\u015f\u0131k s\u00fcre\u00e7 nas\u0131l i\u015fler? Qwen3.5’in mimarisi, genellikle iki ana bile\u015fenin birle\u015fiminden olu\u015fur: bir <strong>g\u00f6r\u00fcnt\u00fc kodlay\u0131c\u0131 (image encoder)<\/strong> ve bir <strong>metin kodlay\u0131c\u0131\/dil modeli (text encoder\/language model)<\/strong>. G\u00f6r\u00fcnt\u00fc kodlay\u0131c\u0131, bir g\u00f6rseli al\u0131r ve onu modelin anlayabilece\u011fi say\u0131sal bir temsiline, yani bir “g\u00f6mme” (embedding) vekt\u00f6r\u00fcne d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu g\u00f6mme vekt\u00f6r\u00fc, g\u00f6r\u00fcnt\u00fcn\u00fcn i\u00e7eri\u011findeki \u00f6nemli \u00f6zellikleri (nesneler, renkler, dokular, \u015fekiller vb.) kompakt bir \u015fekilde \u00f6zetler. Benzer \u015fekilde, metin kodlay\u0131c\u0131, bir metin par\u00e7as\u0131n\u0131 al\u0131r ve onu da ayn\u0131 boyutlardaki bir say\u0131sal g\u00f6mme vekt\u00f6r\u00fcne \u00e7evirir. Bu sayede, hem g\u00f6rsel hem de metinsel veriler, modelin i\u00e7 d\u00fcnyas\u0131nda kar\u015f\u0131la\u015ft\u0131r\u0131labilir ve birlikte i\u015flenebilir hale gelir.<\/p>\n<p>Bu iki g\u00f6mme vekt\u00f6r\u00fc daha sonra birle\u015ftirilir ve modelin ana dil modeline (genellikle bir transformat\u00f6r tabanl\u0131 mimari) beslenir. Dil modeli, bu birle\u015ftirilmi\u015f bilgiyi kullanarak \u00e7e\u015fitli g\u00f6revleri yerine getirir. \u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fcy\u00fc a\u00e7\u0131klayan bir metin \u00fcretebilir, bir g\u00f6rselle ilgili bir soruya yan\u0131t verebilir veya hatta verilen bir metin ve g\u00f6rsele dayanarak yeni bir g\u00f6rsel i\u00e7eri\u011fi tan\u0131mlayabilir. Qwen3.5’in g\u00fcc\u00fc, bu iki modalite aras\u0131ndaki derin ili\u015fkileri \u00f6\u011frenme yetene\u011finden gelir. Model, milyonlarca g\u00f6r\u00fcnt\u00fc-metin \u00e7ifti \u00fczerinde e\u011fitilerek, bir kedinin neye benzedi\u011fini ve “kedi” kelimesinin ne anlama geldi\u011fini e\u015fle\u015ftirmeyi \u00f6\u011frenir; dahas\u0131, “uyuyan kedi” gibi daha karma\u015f\u0131k kavramlar\u0131 da g\u00f6rsel ba\u011flamda yorumlayabilir.<\/p>\n<p>Qwen serisi modeller, \u00f6zellikle geni\u015f \u00f6l\u00e7ekli veri setleri \u00fczerinde yap\u0131lan \u00f6n e\u011fitim (pre-training) s\u00fcre\u00e7leri sayesinde etkileyici performans sergiler. Qwen3.5, bu miras\u0131n bir par\u00e7as\u0131 olarak, sadece genel nesne tan\u0131ma veya basit g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klamalar\u0131n\u0131n \u00f6tesine ge\u00e7erek, daha n\u00fcansl\u0131 ve ba\u011flama duyarl\u0131 \u00e7\u0131kt\u0131lar \u00fcretebilir. \u00d6rne\u011fin, bir t\u0131bbi g\u00f6r\u00fcnt\u00fcdeki belirli anormallikleri bir doktorun anlayaca\u011f\u0131 dilde a\u00e7\u0131klayabilir veya bir m\u00fchendislik \u00e7izimindeki detaylar\u0131 teknik terimlerle yorumlayabilir. Bu, modelin sadece “ne oldu\u011funu” de\u011fil, ayn\u0131 zamanda “neden \u00f6nemli oldu\u011funu” da bir dereceye kadar anlamas\u0131n\u0131 sa\u011flar. Bu t\u00fcr yetenekler, Qwen3.5’i sadece bir ara\u015ft\u0131rma arac\u0131 olmaktan \u00e7\u0131kar\u0131p, ger\u00e7ek d\u00fcnya problemlerini \u00e7\u00f6zebilen pratik bir uygulama haline getirir. Modelin esnek mimarisi ve geni\u015f kapsaml\u0131 e\u011fitimi, onu gelecekteki yapay zeka uygulamalar\u0131 i\u00e7in vazge\u00e7ilmez bir temel ta\u015f yapmaktad\u0131r.<\/p>\n<h2>Qwen3.5 Geli\u015ftirme Ortam\u0131 Nas\u0131l Kurulur?<\/h2>\n<p>Qwen3.5 ile \u00e7al\u0131\u015fmaya ba\u015flamadan \u00f6nce, gerekli geli\u015ftirme ortam\u0131n\u0131 kurman\u0131z gerekiyor. Bu s\u00fcre\u00e7, genellikle Python programlama dilini ve baz\u0131 k\u00fct\u00fcphaneleri y\u00fcklemeyi i\u00e7erir. Endi\u015felenmeyin, ad\u0131mlar olduk\u00e7a basit ve anla\u015f\u0131l\u0131rd\u0131r. Bu b\u00f6l\u00fcmde, Qwen3.5’i kullanmaya ba\u015flamak i\u00e7in ihtiyac\u0131n\u0131z olan her \u015feyi ad\u0131m ad\u0131m kuraca\u011f\u0131z.<\/p>\n<h3>Gerekli \u00d6nko\u015fullar Nelerdir?<\/h3>\n<p>Qwen3.5 ile \u00e7al\u0131\u015fmak i\u00e7in temel olarak \u015funlara ihtiyac\u0131n\u0131z olacak:<\/p>\n<ol>\n<li><strong>Python:<\/strong> Modern yapay zeka ve makine \u00f6\u011frenimi projelerinin dili. Genellikle 3.8 veya \u00fczeri s\u00fcr\u00fcmler \u00f6nerilir.<\/li>\n<li><strong><code>pip<\/code>:<\/strong> Python paket y\u00f6neticisi. Python ile birlikte gelir.<\/li>\n<li><strong>Sanalla\u015ft\u0131rma Ortam\u0131 (\u00d6nerilir):<\/strong> Projeleriniz aras\u0131nda ba\u011f\u0131ml\u0131l\u0131k \u00e7ak\u0131\u015fmalar\u0131n\u0131 \u00f6nlemek i\u00e7in sanal ortam kullanmak iyi bir pratiktir. <code>venv<\/code> veya <code>conda<\/code> kullanabilirsiniz.<\/li>\n<li><strong>API Eri\u015fimi ve Anahtar\u0131:<\/strong> Qwen3.5 modeline eri\u015fim genellikle bir API arac\u0131l\u0131\u011f\u0131yla sa\u011flan\u0131r. Alibaba Cloud veya Hugging Face gibi platformlardan bir API anahtar\u0131 alman\u0131z gerekebilir. Bu anahtar, modelle ileti\u015fim kurarken kimli\u011finizi do\u011frular.<\/li>\n<\/ol>\n<h3>Ad\u0131m Ad\u0131m Kurulum Rehberi<\/h3>\n<h4>Ad\u0131m 1: Python ve Sanal Ortam Kurulumu<\/h4>\n<p>\u00d6ncelikle, sisteminizde Python’\u0131n kurulu oldu\u011fundan emin olun. Komut istemcinizi (Windows) veya terminalinizi (macOS\/Linux) a\u00e7\u0131n ve a\u015fa\u011f\u0131daki komutu \u00e7al\u0131\u015ft\u0131r\u0131n:<\/p>\n<pre><code>\npython --version\n<\/code><\/pre>\n<p>E\u011fer Python kurulu de\u011filse veya eski bir s\u00fcr\u00fcmse, Python’\u0131n resmi web sitesinden (python.org) en son s\u00fcr\u00fcm\u00fc indirip kurun. Kurulum s\u0131ras\u0131nda “Add Python to PATH” se\u00e7ene\u011fini i\u015faretlemeyi unutmay\u0131n.<\/p>\n<p>\u015eimdi bir sanal ortam olu\u015ftural\u0131m. Bu, projenizin ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131 sistem genelindeki Python kurulumunuzdan izole etmenizi sa\u011flar:<\/p>\n<pre><code>\nmkdir qwen35_proje\ncd qwen35_proje\npython -m venv venv\n<\/code><\/pre>\n<p>Bu komutlar, projeniz i\u00e7in <code>qwen35_proje<\/code> ad\u0131nda bir klas\u00f6r olu\u015fturur ve i\u00e7ine <code>venv<\/code> ad\u0131nda bir sanal ortam kurar. Sanal ortam\u0131 etkinle\u015ftirmek i\u00e7in:<\/p>\n<ul>\n<li><strong>Windows:<\/strong><\/li>\n<pre><code>\nvenv\\Scripts\\activate\n <\/code><\/pre>\n<li><strong>macOS\/Linux:<\/strong><\/li>\n<pre><code>\nsource venv\/bin\/activate\n <\/code><\/pre>\n<\/ul>\n<p>Terminalinizde veya komut istemcinizde <code>(venv)<\/code> ibaresini g\u00f6rmeniz, sanal ortam\u0131n ba\u015far\u0131yla etkinle\u015ftirildi\u011fi anlam\u0131na gelir.<\/p>\n<h4>Ad\u0131m 2: Gerekli Python K\u00fct\u00fcphanelerini Y\u00fckleme<\/h4>\n<p>Qwen3.5 modeline eri\u015fmek i\u00e7in genellikle Hugging Face Transformers k\u00fct\u00fcphanesi ve Alibaba Cloud’un ilgili SDK’s\u0131 kullan\u0131l\u0131r. Bu makalede, genellikle Qwen modellerine eri\u015fim sa\u011flayan genel bir yakla\u015f\u0131m olan Transformers k\u00fct\u00fcphanesini temel alaca\u011f\u0131z. Ayr\u0131ca, g\u00f6r\u00fcnt\u00fc i\u015fleme i\u00e7in <code>Pillow<\/code> veya <code>opencv-python<\/code> gibi k\u00fct\u00fcphaneler de faydal\u0131 olabilir.<\/p>\n<pre><code>\npip install transformers torch pillow\n<\/code><\/pre>\n<p>Bu komut, Qwen3.5 gibi b\u00fcy\u00fck modellerle \u00e7al\u0131\u015fmak i\u00e7in gerekli olan temel k\u00fct\u00fcphaneleri y\u00fckleyecektir. <code>torch<\/code>, derin \u00f6\u011frenme i\u015flemleri i\u00e7in arka planda \u00e7al\u0131\u015fan bir k\u00fct\u00fcphanedir. E\u011fer GPU h\u0131zland\u0131rmas\u0131 kullanmak istiyorsan\u0131z, <code>torch<\/code>‘u CUDA deste\u011fiyle y\u00fcklemeniz gerekebilir. Bunun i\u00e7in PyTorch’un resmi web sitesindeki talimatlar\u0131 izlemeniz \u00f6nerilir.<\/p>\n<h4>Ad\u0131m 3: API Anahtar\u0131n\u0131 Ayarlama<\/h4>\n<p>Qwen3.5 modeline eri\u015fim i\u00e7in bir API anahtar\u0131na ihtiyac\u0131n\u0131z olacak. Bu anahtar\u0131 genellikle ortam de\u011fi\u015fkeni olarak ayarlamak, kodunuzun daha g\u00fcvenli ve ta\u015f\u0131nabilir olmas\u0131n\u0131 sa\u011flar. \u00d6rnek olarak, bir <code>.env<\/code> dosyas\u0131 kullanabilir veya do\u011frudan terminalinizde ayarlayabilirsiniz. Genellikle Hugging Face Hub \u00fczerinden modelleri \u00e7ekerken do\u011frudan bir anahtar gerekmez, ancak \u00fccretli API hizmetleri i\u00e7in bu ad\u0131m\u0131 uygulaman\u0131z \u015fartt\u0131r. A\u015fa\u011f\u0131daki \u00f6rnek, bir ortam de\u011fi\u015fkenini nas\u0131l ayarlayaca\u011f\u0131n\u0131z\u0131 g\u00f6sterir:<\/p>\n<ul>\n<li><strong>Linux\/macOS:<\/strong><\/li>\n<pre><code>\nexport ALIBABA_CLOUD_ACCESS_KEY_ID=\"Sizin_Access_Key_ID'niz\"\nexport ALIBABA_CLOUD_ACCESS_KEY_SECRET=\"Sizin_Access_Key_Secret'iniz\"\n <\/code><\/pre>\n<li><strong>Windows (Komut \u0130stemi):<\/strong><\/li>\n<pre><code>\nset ALIBABA_CLOUD_ACCESS_KEY_ID=\"Sizin_Access_Key_ID'niz\"\nset ALIBABA_CLOUD_ACCESS_KEY_SECRET=\"Sizin_Access_Key_Secret'iniz\"\n <\/code><\/pre>\n<\/ul>\n<p>Bu de\u011fi\u015fkenler, Python kodunuzda <code>os.environ.get(\"ALIBABA_CLOUD_ACCESS_KEY_ID\")<\/code> \u015feklinde eri\u015filebilir olacakt\u0131r. Alibaba Cloud veya benzeri bir platformda bir hesap olu\u015fturarak bu anahtarlar\u0131 edinebilirsiniz.<\/p>\n<p>Art\u0131k Qwen3.5 ile \u00e7al\u0131\u015fmaya ba\u015flamak i\u00e7in temel geli\u015ftirme ortam\u0131n\u0131z haz\u0131r. Bir sonraki b\u00f6l\u00fcmde, bu ortam\u0131 kullanarak ilk g\u00f6rsel analiz ve metin \u00fcretimi \u00f6rneklerimizi ger\u00e7ekle\u015ftirece\u011fiz.<\/p>\n<h2>Qwen3.5 ile G\u00f6rsel Analiz ve Metin \u00dcretimi Nas\u0131l Yap\u0131l\u0131r?<\/h2>\n<p>Geli\u015ftirme ortam\u0131m\u0131z\u0131 ba\u015far\u0131yla kurduktan sonra, Qwen3.5’in ger\u00e7ek g\u00fcc\u00fcn\u00fc deneyimleme zaman\u0131 geldi. Bu b\u00f6l\u00fcmde, modelin temel yeteneklerini g\u00f6steren iki \u00f6nemli uygulamay\u0131 ad\u0131m ad\u0131m inceleyece\u011fiz: g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama (image captioning) ve g\u00f6rsel soru cevaplama (visual question answering – VQA). Her iki uygulama i\u00e7in de kod \u00f6rnekleri sunacak ve ger\u00e7ek d\u00fcnya senaryolar\u0131nda nas\u0131l kullan\u0131labilece\u011fini tart\u0131\u015faca\u011f\u0131z.<\/p>\n<h3>G\u00f6r\u00fcnt\u00fc A\u00e7\u0131klama (Image Captioning) \u00d6rne\u011fi<\/h3>\n<p>G\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama, bir g\u00f6rselin i\u00e7eri\u011fini do\u011fal dilde \u00f6zetleyen bir metin \u00fcretme g\u00f6revidir. Bu, g\u00f6rme engelliler i\u00e7in eri\u015filebilirlik sa\u011flamaktan, e-ticaret siteleri i\u00e7in otomatik \u00fcr\u00fcn a\u00e7\u0131klamalar\u0131 olu\u015fturmaya kadar bir\u00e7ok alanda de\u011ferli bir yetenektir. Qwen3.5, karma\u015f\u0131k sahneleri bile do\u011fru ve ba\u011flama uygun bir \u015fekilde a\u00e7\u0131klayabilir.<\/p>\n<h4>Ad\u0131m 1: Modeli Y\u00fckleme ve G\u00f6r\u00fcnt\u00fcy\u00fc Haz\u0131rlama<\/h4>\n<p>\u00d6ncelikle, Qwen3.5 modelini Hugging Face Transformers k\u00fct\u00fcphanesi arac\u0131l\u0131\u011f\u0131yla y\u00fcklememiz gerekiyor. Ayr\u0131ca, a\u00e7\u0131klamak istedi\u011fimiz bir g\u00f6rseli de Python’a aktarmal\u0131y\u0131z. G\u00f6rseli bir URL’den veya yerel bir dosyadan y\u00fckleyebilirsiniz.<\/p>\n<pre><code>\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom PIL import Image\nimport requests\nimport torch\n\n# Qwen3.5 modelini ve tokenizer'\u0131n\u0131 y\u00fckleme\n# Bu ad\u0131m biraz zaman alabilir, model boyutuna ba\u011fl\u0131d\u0131r.\n# Qwen-VL-Chat veya Qwen-VL gibi modelleri kullanabilirsiniz.\n# Model ismini kendi kulland\u0131\u011f\u0131n\u0131z Qwen Vision-Language modeline g\u00f6re g\u00fcncelleyin.\nmodel_id = \"Qwen\/Qwen-VL-Chat\" # \u00d6rnek model ID'si\ntokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)\nmodel = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).eval()\n\n# \u00d6rnek bir g\u00f6rsel y\u00fckleme (bir URL'den)\n# Kendi g\u00f6rsel URL'nizi veya yerel dosya yolunuzu kullanabilirsiniz.\nimage_url = \"https:\/\/www.iloveimg.com\/img\/iloveimg.jpg\" # \u00d6rnek bir g\u00f6rsel URL'si\nimage = Image.open(requests.get(image_url, stream=True).raw).convert(\"RGB\")\n\n# E\u011fer yerel bir dosyadan y\u00fckleyecekseniz:\n# image_path = \"path\/to\/your\/image.jpg\"\n# image = Image.open(image_path).convert(\"RGB\")\n<\/code><\/pre>\n<p>Bu kod blo\u011fu, Qwen-VL-Chat modelini ve ili\u015fkili tokenla\u015ft\u0131r\u0131c\u0131s\u0131n\u0131 y\u00fckler. Ayr\u0131ca, internetten bir g\u00f6rseli indirir ve Pillow k\u00fct\u00fcphanesiyle i\u015flenebilir hale getirir.<\/p>\n<h4>Ad\u0131m 2: G\u00f6rseli Modele G\u00f6nderme ve A\u00e7\u0131klama \u00dcretme<\/h4>\n<p>\u015eimdi, y\u00fckledi\u011fimiz g\u00f6rseli modele g\u00f6nderip bir a\u00e7\u0131klama \u00fcretmesini isteyebiliriz. Qwen modelleri genellikle belirli bir prompt (istem) format\u0131 bekler. G\u00f6rseli, metin istemiyle birlikte modele iletiriz.<\/p>\n<pre><code>\n# Modelin bekledi\u011fi prompt format\u0131n\u0131 olu\u015fturma\n# Qwen-VL-Chat i\u00e7in genellikle <img_path> veya do\u011frudan g\u00f6r\u00fcnt\u00fc objesi kullan\u0131l\u0131r.\n# Burada, modelin g\u00f6r\u00fcnt\u00fcye dayal\u0131 bir a\u00e7\u0131klama yapmas\u0131n\u0131 istedi\u011fimizi belirtiyoruz.\nquery = \"Bu g\u00f6rselde ne var?\" # G\u00f6rsel hakk\u0131nda bir soru veya a\u00e7\u0131klama istemi\ninputs = tokenizer.apply_chat_template(\n [{\"role\": \"user\", \"content\": [{\"image\": image}, {\"text\": query}]}],\n tokenize=False,\n add_generation_prompt=True\n)\n\n# Girdileri token'lara d\u00f6n\u00fc\u015ft\u00fcrme\ninput_ids = tokenizer(inputs, return_tensors=\"pt\").input_ids\n\n# Modeli kullanarak a\u00e7\u0131klama \u00fcretme\nwith torch.no_grad():\n output = model.generate(input_ids, max_new_tokens=200, do_sample=False) # max_new_tokens ile \u00e7\u0131kt\u0131n\u0131n uzunlu\u011funu belirleyebiliriz.\n\n# \u00dcretilen \u00e7\u0131kt\u0131y\u0131 decode etme\nresponse = tokenizer.decode(output[0], skip_special_tokens=True)\n\n# Sadece modelin cevab\u0131n\u0131 almak i\u00e7in prompt k\u0131sm\u0131n\u0131 temizleme\n# Bu k\u0131s\u0131m modelin \u00e7\u0131kt\u0131s\u0131na g\u00f6re de\u011fi\u015febilir.\n# Genellikle 'user' ve 'assistant' rollerini ay\u0131rarak yap\u0131l\u0131r.\nif \"assistant\" in response:\n response = response.split(\"assistant\")[1].strip()\n\nprint(f\"G\u00f6rsel A\u00e7\u0131klamas\u0131: {response}\")\n<\/code><\/pre>\n<p>Bu kod par\u00e7as\u0131, Qwen3.5’in g\u00f6rsel a\u00e7\u0131klama yetene\u011fini sergiliyor. Model, g\u00f6rseli analiz ederek “Bu g\u00f6rselde ne var?” sorusuna bir yan\u0131t \u00fcretiyor. \u00c7\u0131kt\u0131, g\u00f6rseldeki ana unsurlar\u0131 ve eylemleri \u00f6zetleyen bir c\u00fcmle veya k\u0131sa bir paragraf olacakt\u0131r.<\/p>\n<p><strong>Ger\u00e7ek D\u00fcnya Senaryosu:<\/strong> E-ticaret platformlar\u0131, bu yetene\u011fi kullanarak \u00fcr\u00fcn g\u00f6rselleri i\u00e7in otomatik olarak SEO uyumlu a\u00e7\u0131klamalar olu\u015fturabilir. Bu, manuel yaz\u0131m ihtiyac\u0131n\u0131 azalt\u0131r ve \u00fcr\u00fcnlerin arama motorlar\u0131nda daha iyi s\u0131ralanmas\u0131na yard\u0131mc\u0131 olur. Ayr\u0131ca, sosyal medya i\u00e7erik \u00fcreticileri de g\u00f6rsellerine h\u0131zl\u0131 ve etkili a\u00e7\u0131klamalar ekleyebilirler.<\/p>\n<h3>G\u00f6rsel Soru Cevaplama (Visual Question Answering – VQA) \u00d6rne\u011fi<\/h3>\n<p>VQA, bir kullan\u0131c\u0131 taraf\u0131ndan sorulan bir soruya, bir g\u00f6rseldeki bilgileri kullanarak yan\u0131t verme g\u00f6revidir. Bu, modelin sadece g\u00f6rseldeki nesneleri tan\u0131makla kalmay\u0131p, ayn\u0131 zamanda g\u00f6rseldeki ba\u011flam\u0131 ve nesneler aras\u0131ndaki ili\u015fkileri de anlamas\u0131n\u0131 gerektirir.<\/p>\n<h4>Ad\u0131m 1: Modeli Y\u00fckleme ve G\u00f6rseli Haz\u0131rlama (\u00d6nceki Ad\u0131mla Ayn\u0131)<\/h4>\n<p>VQA i\u00e7in de ayn\u0131 model y\u00fckleme ve g\u00f6rsel haz\u0131rlama ad\u0131mlar\u0131n\u0131 kullanaca\u011f\u0131z. E\u011fer \u00f6nceki b\u00f6l\u00fcmdeki kodu \u00e7al\u0131\u015ft\u0131rd\u0131ysan\u0131z, bu k\u0131sm\u0131 tekrar etmenize gerek yoktur.<\/p>\n<pre><code>\n# Model ve tokenizer zaten y\u00fckl\u00fc varsay\u0131l\u0131yor.\n# image objesi de zaten y\u00fckl\u00fc varsay\u0131l\u0131yor.\n# E\u011fer y\u00fckl\u00fc de\u011filse, \u00f6nceki kod blo\u011funu \u00e7al\u0131\u015ft\u0131r\u0131n.\n<\/code><\/pre>\n<h4>Ad\u0131m 2: G\u00f6rsel ve Soru ile Modeli Sorgulama<\/h4>\n<p>\u015eimdi, bir g\u00f6rselle birlikte belirli bir soru sorarak modelden yan\u0131t alal\u0131m. \u00d6rne\u011fin, bir odan\u0131n g\u00f6rselini g\u00f6sterip “Bu odada ka\u00e7 sandalye var?” gibi bir soru sorabiliriz.<\/p>\n<pre><code>\n# VQA i\u00e7in yeni bir soru tan\u0131mlama\nvqa_query = \"Bu g\u00f6rseldeki ana renkler nelerdir?\" # G\u00f6rselle ilgili bir soru\n\n# Modelin bekledi\u011fi prompt format\u0131n\u0131 olu\u015fturma\nvqa_inputs = tokenizer.apply_chat_template(\n [{\"role\": \"user\", \"content\": [{\"image\": image}, {\"text\": vqa_query}]}],\n tokenize=False,\n add_generation_prompt=True\n)\n\n# Girdileri token'lara d\u00f6n\u00fc\u015ft\u00fcrme\nvqa_input_ids = tokenizer(vqa_inputs, return_tensors=\"pt\").input_ids\n\n# Modeli kullanarak cevap \u00fcretme\nwith torch.no_grad():\n vqa_output = model.generate(vqa_input_ids, max_new_tokens=100, do_sample=False)\n\n# \u00dcretilen \u00e7\u0131kt\u0131y\u0131 decode etme\nvqa_response = tokenizer.decode(vqa_output[0], skip_special_tokens=True)\n\n# Sadece modelin cevab\u0131n\u0131 almak i\u00e7in prompt k\u0131sm\u0131n\u0131 temizleme\nif \"assistant\" in vqa_response:\n vqa_response = vqa_response.split(\"assistant\")[1].strip()\n\nprint(f\"Soru: {vqa_query}\")\nprint(f\"Cevap: {vqa_response}\")\n<\/code><\/pre>\n<p>Bu kod, Qwen3.5’in bir g\u00f6rseldeki bilgilere dayanarak sorular\u0131 nas\u0131l yan\u0131tlad\u0131\u011f\u0131n\u0131 g\u00f6sterir. Model, g\u00f6rseldeki nesneleri, renkleri, konumlar\u0131 ve di\u011fer \u00f6zellikleri anlayarak soruyu ba\u011flam\u0131na uygun bir \u015fekilde yan\u0131tlar.<\/p>\n<p><strong>Ger\u00e7ek D\u00fcnya Senaryosu:<\/strong> T\u0131bbi g\u00f6r\u00fcnt\u00fcleme alan\u0131nda, doktorlar bir X-ray g\u00f6r\u00fcnt\u00fcs\u00fcne bak\u0131p “Bu b\u00f6lgede bir k\u0131r\u0131k belirtisi var m\u0131?” gibi sorular sorarak Qwen3.5’ten \u00f6n analiz alabilirler. M\u00fc\u015fteri hizmetleri botlar\u0131, bir kullan\u0131c\u0131n\u0131n g\u00f6nderdi\u011fi \u00fcr\u00fcn foto\u011fraf\u0131 hakk\u0131nda “Bu \u00fcr\u00fcn\u00fcn garantisi var m\u0131?” gibi sorulara do\u011frudan g\u00f6rseldeki seri numaras\u0131n\u0131 veya model bilgisini okuyarak yan\u0131t verebilir. Bu, hem zaman kazand\u0131r\u0131r hem de insan hatas\u0131n\u0131 azalt\u0131r.<\/p>\n<p>Bu \u00f6rnekler, Qwen3.5’in \u00e7ok y\u00f6nl\u00fc yeteneklerinin sadece k\u00fc\u00e7\u00fck bir k\u0131sm\u0131n\u0131 g\u00f6stermektedir. Modelin g\u00fcc\u00fc, sizin yarat\u0131c\u0131l\u0131\u011f\u0131n\u0131zla birle\u015fti\u011finde s\u0131n\u0131rs\u0131z uygulama alanlar\u0131na d\u00f6n\u00fc\u015febilir. Bir sonraki b\u00f6l\u00fcmde, daha ileri d\u00fczey uygulamalar ve performans ipu\u00e7lar\u0131 hakk\u0131nda konu\u015faca\u011f\u0131z.<\/p>\n<h2>Qwen3.5 ile \u0130leri D\u00fczey Uygulamalar ve Entegrasyon \u0130pu\u00e7lar\u0131 Nelerdir?<\/h2>\n<p>Qwen3.5’in temel yeteneklerini anlad\u0131ktan sonra, \u015fimdi modelin potansiyelini daha da geni\u015fletecek ileri d\u00fczey uygulamalara ve entegrasyon stratejilerine odaklanal\u0131m. Bu b\u00f6l\u00fcm, daha karma\u015f\u0131k senaryolar i\u00e7in ipu\u00e7lar\u0131 sunacak ve performans optimizasyonu hakk\u0131nda bilgiler verecektir.<\/p>\n<h3>Farkl\u0131 Kullan\u0131m Senaryolar\u0131 ve \u00d6zelle\u015ftirmeler<\/h3>\n<p>Qwen3.5’in \u00e7ok modlu yap\u0131s\u0131, onu sadece basit g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama veya soru cevaplaman\u0131n \u00f6tesinde bir\u00e7ok alanda kullan\u0131labilir k\u0131lar:<\/p>\n<ol>\n<li><strong>Video Analizi (Kare Kare):<\/strong> Qwen3.5, do\u011frudan video i\u015fleyemese de, videolar\u0131 kare kare analiz ederek dinamik i\u00e7erikler hakk\u0131nda bilgi sa\u011flayabilir. Her bir video karesini bir g\u00f6r\u00fcnt\u00fc olarak i\u015fleyebilir ve zaman i\u00e7indeki de\u011fi\u015fiklikleri veya olaylar\u0131 tespit edebilir. \u00d6rne\u011fin, bir g\u00fcvenlik kameras\u0131ndan gelen videoyu analiz ederek \u015f\u00fcpheli hareketleri veya belirli nesnelerin (\u00f6rne\u011fin, b\u0131rak\u0131lm\u0131\u015f bir \u00e7anta) ortaya \u00e7\u0131k\u0131\u015f\u0131n\u0131 otomatik olarak raporlayabilir. Bu, \u00f6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli g\u00f6zetim sistemleri ve spor analizi i\u00e7in de\u011ferli olabilir.<\/li>\n<li><strong>Belge \u0130\u015fleme ve Anlama:<\/strong> Taranm\u0131\u015f belgeler, faturalar, formlar veya akademik makaleler gibi g\u00f6rsel tabanl\u0131 metinleri i\u015flemek i\u00e7in Qwen3.5 kullan\u0131labilir. Model, belgedeki metinleri okuyabilir, tablolar\u0131 anlayabilir ve g\u00f6rsel d\u00fczeni yorumlayarak belgenin genel i\u00e7eri\u011fini \u00f6zetleyebilir veya belirli bilgileri \u00e7\u0131karabilir. \u00d6rne\u011fin, bir fatura g\u00f6rselinden otomatik olarak \u00fcr\u00fcn adlar\u0131n\u0131, fiyatlar\u0131n\u0131 ve toplam tutar\u0131 \u00e7ekebilir.<\/li>\n<li><strong>Otonom Sistemler ve Robotik:<\/strong> Otonom ara\u00e7lar ve robotlar, \u00e7evrelerini anlamak i\u00e7in s\u00fcrekli olarak g\u00f6rsel ve sens\u00f6r verilerini i\u015fler. Qwen3.5, bu sistemlerin kameralar\u0131ndan gelen g\u00f6r\u00fcnt\u00fcleri yorumlayarak nesne tan\u0131ma, durum tespiti ve hatta karma\u015f\u0131k senaryolar\u0131 anlamalar\u0131na yard\u0131mc\u0131 olabilir. Bir robotun bir engeli nas\u0131l a\u015faca\u011f\u0131n\u0131 veya bir nesneyi nas\u0131l manip\u00fcle edece\u011fini anlamas\u0131na yard\u0131mc\u0131 olacak g\u00f6rsel-dilsel geri bildirimler sa\u011flayabilir.<\/li>\n<li><strong>\u0130\u00e7erik Moderasyonu:<\/strong> Kullan\u0131c\u0131 taraf\u0131ndan olu\u015fturulan i\u00e7eri\u011fi (UGC) denetlemek i\u00e7in Qwen3.5 kullan\u0131labilir. Hem g\u00f6rsel hem de metinsel i\u00e7eri\u011fi analiz ederek uygunsuz, zararl\u0131 veya spam i\u00e7erikleri otomatik olarak tespit edebilir. Bu, sosyal medya platformlar\u0131 ve forumlar i\u00e7in hayati \u00f6neme sahiptir.<\/li>\n<\/ol>\n<h3>Performans Optimizasyonu \u0130pu\u00e7lar\u0131<\/h3>\n<p>B\u00fcy\u00fck dil modelleri (LLM) ve VLM’ler genellikle yo\u011fun hesaplama kaynaklar\u0131 gerektirir. Qwen3.5 uygulamalar\u0131n\u0131z\u0131n daha h\u0131zl\u0131 ve verimli \u00e7al\u0131\u015fmas\u0131 i\u00e7in baz\u0131 optimizasyon teknikleri \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>Batch \u0130\u015fleme (Batch Processing):<\/strong> Birden fazla g\u00f6r\u00fcnt\u00fcy\u00fc veya metin istemini ayn\u0131 anda modele g\u00f6ndermek, tek tek g\u00f6ndermekten daha verimlidir. GPU’lar, paralel i\u015flem yapma yetenekleri sayesinde batch i\u015flemlerinde \u00e7ok daha iyi performans g\u00f6sterir.\n<pre><code>\n# \u00d6rnek batch i\u015fleme\n# Birden fazla g\u00f6rsel ve\/veya istem i\u00e7in input_ids'i birle\u015ftirin\n# \u00d6rne\u011fin, birden fazla g\u00f6rseli listeye ekleyip tek bir input_ids tensor'u olu\u015fturabilirsiniz.\n# Bu k\u0131s\u0131m modelin API'sine g\u00f6re de\u011fi\u015febilir, ancak genel mant\u0131k ayn\u0131d\u0131r.\n\n# images = [image1, image2, image3]\n# queries = [\"ne var?\", \"renkler neler?\", \"ka\u00e7 ki\u015fi var?\"]\n# batch_inputs = []\n# for img, q in zip(images, queries):\n# batch_inputs.append({\"role\": \"user\", \"content\": [{\"image\": img}, {\"text\": q}]})\n\n# # tokenizer.apply_chat_template ile t\u00fcm batch'i i\u015fleyin\n# batched_input_ids = tokenizer.apply_chat_template(\n# batch_inputs,\n# tokenize=True,\n# add_generation_prompt=True,\n# return_tensors=\"pt\",\n# padding=True # Batch i\u00e7in padding \u00f6nemlidir\n# )\n\n# # Modeli \u00e7a\u011f\u0131r\u0131n\n# with torch.no_grad():\n# batched_output = model.generate(batched_input_ids, max_new_tokens=100)\n# # \u00c7\u0131kt\u0131lar\u0131 decode edin ve ay\u0131r\u0131n\n <\/code><\/pre>\n<\/li>\n<li><strong>Model S\u0131k\u0131\u015ft\u0131rma (Quantization):<\/strong> Modelin a\u011f\u0131rl\u0131klar\u0131n\u0131 daha d\u00fc\u015f\u00fck bit hassasiyetine (\u00f6rne\u011fin, 32-bit float yerine 8-bit int) d\u00f6n\u00fc\u015ft\u00fcrmek, model boyutunu k\u00fc\u00e7\u00fclt\u00fcr ve \u00e7\u0131kar\u0131m (inference) h\u0131z\u0131n\u0131 art\u0131rabilir. Bu, genellikle bir miktar do\u011fruluk kayb\u0131na yol a\u00e7sa da, \u00e7o\u011fu uygulama i\u00e7in kabul edilebilir bir de\u011fi\u015f toku\u015ftur. Hugging Face Transformers k\u00fct\u00fcphanesi, <code>load_in_8bit=True<\/code> gibi parametrelerle bu \u00f6zelli\u011fi destekler.\n<pre><code>\n# \u00d6rne\u011fin, 8-bit s\u0131k\u0131\u015ft\u0131rma ile modeli y\u00fckleme\n# model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, load_in_8bit=True).eval()\n <\/code><\/pre>\n<\/li>\n<li><strong>\u00d6nbellekleme (Caching):<\/strong> S\u0131k kullan\u0131lan istemler veya ara sonu\u00e7lar i\u00e7in \u00f6nbellekleme kullanmak, tekrarlayan hesaplamalar\u0131n \u00f6n\u00fcne ge\u00e7erek performans\u0131 art\u0131rabilir. \u00d6zellikle web servislerinde, ayn\u0131 g\u00f6rsel veya soru tekrar tekrar geldi\u011finde modelin her seferinde ba\u015ftan hesaplama yapmas\u0131n\u0131 engeller.<\/li>\n<li><strong>GPU H\u0131zland\u0131rmas\u0131:<\/strong> Qwen3.5 gibi b\u00fcy\u00fck modellerin \u00e7\u0131kar\u0131m\u0131n\u0131 CPU \u00fczerinde yapmak \u00e7ok yava\u015f olabilir. M\u00fcmk\u00fcnse, bir GPU kullanmak performans\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131racakt\u0131r. PyTorch’u CUDA deste\u011fiyle kurdu\u011funuzdan ve model ile tens\u00f6rleri GPU’ya ta\u015f\u0131d\u0131\u011f\u0131n\u0131zdan emin olun (<code>.to(\"cuda\")<\/code>).<\/li>\n<\/ul>\n<h3>Di\u011fer Sistemlerle Entegrasyon<\/h3>\n<p>Qwen3.5’i mevcut sistemlerinize entegre etmek, onun de\u011ferini art\u0131racakt\u0131r:<\/p>\n<ul>\n<li><strong>Web Uygulamalar\u0131 (Flask\/Django):<\/strong> Qwen3.5’i bir RESTful API olarak sunan bir web servisi (\u00f6rne\u011fin Flask veya Django ile) olu\u015fturabilirsiniz. Bu sayede, farkl\u0131 platformlardan (web siteleri, mobil uygulamalar vb.) modele kolayca eri\u015filebilir. Kullan\u0131c\u0131lar bir g\u00f6rsel y\u00fckleyebilir ve a\u00e7\u0131klamas\u0131n\u0131 veya yan\u0131t\u0131n\u0131 an\u0131nda alabilirler.<\/li>\n<li><strong>Mobil Uygulamalar:<\/strong> Mobil uygulamalar, genellikle bulut tabanl\u0131 API’ler arac\u0131l\u0131\u011f\u0131yla Qwen3.5 gibi modellere eri\u015fir. G\u00f6r\u00fcnt\u00fc mobil cihazdan y\u00fcklenir, API’ye g\u00f6nderilir, model i\u015fler ve sonu\u00e7 mobil cihaza geri g\u00f6nderilir. Bu, cihaz\u0131n kendi i\u015flem g\u00fcc\u00fcne ba\u011f\u0131ml\u0131l\u0131\u011f\u0131 azalt\u0131r.<\/li>\n<li><strong>Veritabanlar\u0131 ve Veri Ambarlar\u0131:<\/strong> Qwen3.5’ten elde edilen g\u00f6rsel a\u00e7\u0131klamalar\u0131 veya etiketleri, veritabanlar\u0131n\u0131zdaki ilgili g\u00f6rsellerle ili\u015fkilendirebilirsiniz. Bu, g\u00f6rsel aramay\u0131 ve veri analizi yeteneklerini zenginle\u015ftirir. \u00d6rne\u011fin, bir g\u00f6rsel arama motoru, sadece dosya ad\u0131na g\u00f6re de\u011fil, ayn\u0131 zamanda Qwen3.5 taraf\u0131ndan \u00fcretilen detayl\u0131 a\u00e7\u0131klamalara g\u00f6re de arama yapabilir.<\/li>\n<\/ul>\n<p>Qwen3.5’in bu ileri d\u00fczey yetenekleri ve entegrasyon stratejileri, onu sadece bir yapay zeka modeli olmaktan \u00e7\u0131kar\u0131p, karma\u015f\u0131k sistemlerin ayr\u0131lmaz bir par\u00e7as\u0131 haline getirir. Yarat\u0131c\u0131l\u0131\u011f\u0131n\u0131z\u0131 kullanarak bu modelle neler yapabilece\u011finizi ke\u015ffetmek, ger\u00e7ekten heyecan verici bir s\u00fcre\u00e7 olacakt\u0131r.<\/p>\n<h2>Qwen3.5 Uygulamalar\u0131n\u0131za Mobil Uyumluluk Nas\u0131l Kazand\u0131r\u0131l\u0131r?<\/h2>\n<p>G\u00fcn\u00fcm\u00fcz d\u00fcnyas\u0131nda, kullan\u0131c\u0131lar\u0131n b\u00fcy\u00fck bir \u00e7o\u011funlu\u011fu internete mobil cihazlar \u00fczerinden eri\u015fiyor. Bu nedenle, geli\u015ftirdi\u011finiz Qwen3.5 tabanl\u0131 uygulamalar\u0131n mobil uyumlu olmas\u0131, geni\u015f bir kitleye ula\u015fmak ve iyi bir kullan\u0131c\u0131 deneyimi sunmak i\u00e7in kritik \u00f6neme sahiptir. Qwen3.5 gibi b\u00fcy\u00fck modeller do\u011frudan mobil cihazlarda \u00e7al\u0131\u015fmak i\u00e7in genellikle \u00e7ok b\u00fcy\u00fckt\u00fcr, ancak API tabanl\u0131 yakla\u015f\u0131mlar ve duyarl\u0131 tasar\u0131m prensipleriyle mobil uyumluluk kolayca sa\u011flanabilir.<\/p>\n<h3>API Tabanl\u0131 Yakla\u015f\u0131mlarla Mobil Entegrasyon<\/h3>\n<p>Qwen3.5 gibi g\u00fc\u00e7l\u00fc bir VLM’yi mobil uygulamalara entegre etmenin en yayg\u0131n ve etkili yolu, bir bulut sunucusu \u00fczerinde \u00e7al\u0131\u015fan bir API (Uygulama Programlama Aray\u00fcz\u00fc) kullanmakt\u0131r. Mobil cihaz\u0131n kendisi, modelin a\u011f\u0131r hesaplama y\u00fck\u00fcn\u00fc \u00fcstlenmek yerine, yaln\u0131zca API’ye istek g\u00f6nderir ve yan\u0131t\u0131 i\u015fler. Bu yakla\u015f\u0131m\u0131n avantajlar\u0131 \u015funlard\u0131r:<\/p>\n<ul>\n<li><strong>D\u00fc\u015f\u00fck Cihaz Kayna\u011f\u0131 T\u00fcketimi:<\/strong> Mobil cihaz\u0131n i\u015flem g\u00fcc\u00fc, belle\u011fi ve pil \u00f6mr\u00fc korunur.<\/li>\n<li><strong>Merkezi Model Y\u00f6netimi:<\/strong> Model g\u00fcncellemeleri veya optimizasyonlar\u0131 sunucu taraf\u0131nda yap\u0131l\u0131r ve t\u00fcm mobil uygulamalar an\u0131nda faydalan\u0131r.<\/li>\n<li><strong>Platform Ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131:<\/strong> Hem iOS hem de Android uygulamalar\u0131 ayn\u0131 API’yi kullanabilir.<\/li>\n<\/ul>\n<p>Mobil uygulama, kullan\u0131c\u0131n\u0131n \u00e7ekti\u011fi bir foto\u011fraf\u0131 veya se\u00e7ti\u011fi bir g\u00f6rseli API’ye (\u00f6rne\u011fin, bir RESTful API endpoint’ine) g\u00f6nderir. API, bu g\u00f6rseli Qwen3.5 modeline iletir, model g\u00f6rseli i\u015fler ve bir metin a\u00e7\u0131klamas\u0131 veya soruya yan\u0131t \u00fcretir. \u00dcretilen yan\u0131t, API taraf\u0131ndan mobil uygulamaya geri g\u00f6nderilir ve kullan\u0131c\u0131ya sunulur. Bu s\u00fcre\u00e7 genellikle JSON format\u0131nda veri al\u0131\u015fveri\u015fi ile ger\u00e7ekle\u015fir.<\/p>\n<h3>Duyarl\u0131 Tasar\u0131m Prensipleri ve CSS Media Query \u00d6rnekleri<\/h3>\n<p>E\u011fer Qwen3.5 uygulaman\u0131z bir web aray\u00fcz\u00fcne sahipse (\u00f6rne\u011fin, bir g\u00f6rsel y\u00fckleme ve a\u00e7\u0131klama alma web uygulamas\u0131), bu aray\u00fcz\u00fcn farkl\u0131 ekran boyutlar\u0131na ve cihazlara (masa\u00fcst\u00fc, tablet, mobil telefon) uyum sa\u011flamas\u0131 gerekir. Bu, <strong>duyarl\u0131 web tasar\u0131m\u0131 (responsive web design)<\/strong> prensipleriyle sa\u011flan\u0131r. Duyarl\u0131 tasar\u0131m\u0131n temelinde, CSS Media Query’ler yatar.<\/p>\n<p>Media Query’ler, belirli ko\u015fullar (ekran geni\u015fli\u011fi, cihaz tipi, y\u00f6nlendirme vb.) kar\u015f\u0131land\u0131\u011f\u0131nda farkl\u0131 CSS kurallar\u0131n\u0131n uygulanmas\u0131na olanak tan\u0131r. B\u00f6ylece, tek bir HTML dosyas\u0131 farkl\u0131 cihazlarda en iyi \u015fekilde g\u00f6r\u00fcnt\u00fclenebilir.<\/p>\n<p>A\u015fa\u011f\u0131da, Qwen3.5 uygulaman\u0131z\u0131n web aray\u00fcz\u00fc i\u00e7in kullanabilece\u011finiz basit bir HTML ve CSS Media Query \u00f6rne\u011fi bulunmaktad\u0131r. Bu \u00f6rnek, bir g\u00f6rsel y\u00fckleme alan\u0131n\u0131 ve sonu\u00e7 g\u00f6sterimini farkl\u0131 ekran boyutlar\u0131na g\u00f6re nas\u0131l ayarlayabilece\u011finizi g\u00f6sterir.<\/p>\n<pre><code>\n<!DOCTYPE html>\n<html lang=\"tr\">\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n <title>Qwen3.5 Mobil Uyumlu Uygulama<\/title>\n <style>\n body {\n font-family: Arial, sans-serif;\n margin: 0;\n padding: 20px;\n background-color: inherit; \/* Tema rengini miras al *\/\n color: inherit; \/* Tema rengini miras al *\/\n }\n .container {\n max-width: 960px;\n margin: 0 auto;\n padding: 20px;\n border: 1px solid; \/* Tema rengini miras al *\/\n border-radius: 8px;\n box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);\n }\n h2 {\n text-align: center;\n color: inherit;\n }\n .upload-section, .results-section {\n margin-bottom: 20px;\n padding: 15px;\n border: 1px dashed; \/* Tema rengini miras al *\/\n border-radius: 5px;\n text-align: center;\n }\n img {\n max-width: 100%;\n height: auto;\n display: block;\n margin: 10px auto;\n border: 1px solid; \/* Tema rengini miras al *\/\n border-radius: 4px;\n }\n button {\n padding: 10px 20px;\n background-color: #007bff; \/* \u00d6rnek renk, CSS de\u011fi\u015fkeni ile y\u00f6netilebilir *\/\n color: white;\n border: none;\n border-radius: 5px;\n cursor: pointer;\n font-size: 16px;\n }\n button:hover {\n background-color: #0056b3; \/* \u00d6rnek renk *\/\n }\n \/* Mobil cihazlar i\u00e7in Media Query *\/\n @media (max-width: 768px) {\n .container {\n padding: 10px;\n margin: 0 10px;\n }\n h2 {\n font-size: 1.5em;\n }\n .upload-section, .results-section {\n padding: 10px;\n }\n button {\n width: 100%;\n margin-top: 10px;\n }\n }\n \/* Daha k\u00fc\u00e7\u00fck mobil cihazlar i\u00e7in Media Query *\/\n @media (max-width: 480px) {\n body {\n padding: 10px;\n }\n .container {\n border: none;\n box-shadow: none;\n }\n h2 {\n font-size: 1.2em;\n }\n }\n <\/style>\n<\/head>\n<body>\n <div class=\"container\">\n <h2>Qwen3.5 ile G\u00f6rsel A\u00e7\u0131klama<\/h2>\n\n <div class=\"upload-section\">\n <p>A\u00e7\u0131klamak istedi\u011finiz g\u00f6rseli y\u00fckleyin:<\/p>\n <input type=\"file\" id=\"imageUpload\" accept=\"image\/*\"><br><br>\n <button onclick=\"processImage()\">G\u00f6rseli Analiz Et<\/button>\n <img id=\"uploadedImage\" src=\"#\" alt=\"Y\u00fcklenen G\u00f6rsel\" style=\"display:none;\">\n <\/div>\n\n <div class=\"results-section\">\n <h3>Analiz Sonucu:<\/h3>\n <p id=\"imageDescription\">G\u00f6rsel a\u00e7\u0131klamas\u0131 burada g\u00f6r\u00fcnecektir.<\/p>\n <\/div>\n <\/div>\n\n <script>\n document.getElementById('imageUpload').addEventListener('change', function(event) {\n const file = event.target.files[0];\n if (file) {\n const reader = new FileReader();\n reader.onload = function(e) {\n const imgElement = document.getElementById('uploadedImage');\n imgElement.src = e.target.result;\n imgElement.style.display = 'block';\n }\n reader.readAsDataURL(file);\n }\n });\n\n function processImage() {\n const imgElement = document.getElementById('uploadedImage');\n if (imgElement.src === \"#\" || imgElement.style.display === \"none\") {\n alert(\"L\u00fctfen \u00f6nce bir g\u00f6rsel y\u00fckleyin.\");\n return;\n }\n \n \/\/ Burada g\u00f6rseli al\u0131p Qwen3.5 API'nize g\u00f6nderme mant\u0131\u011f\u0131 yer alacak.\n \/\/ Bu sadece bir \u00f6rnek fonksiyonudur, ger\u00e7ek API \u00e7a\u011fr\u0131s\u0131 gerektirir.\n document.getElementById('imageDescription').innerText = \"G\u00f6rsel analiz ediliyor... L\u00fctfen bekleyin.\";\n\n \/\/ \u00d6rnek: API \u00e7a\u011fr\u0131s\u0131 sim\u00fclasyonu\n setTimeout(() => {\n document.getElementById('imageDescription').innerText = \"Bu g\u00f6rselde, g\u00fcne\u015fli bir g\u00fcnde ye\u015fil bir tepenin \u00fczerinde duran bir koyun s\u00fcr\u00fcs\u00fc g\u00f6r\u00fcl\u00fcyor.\";\n }, 2000);\n }\n <\/script>\n<\/body>\n<\/html>\n<\/code><\/pre>\n<p>Yukar\u0131daki CSS, <code>max-width: 768px<\/code> ve <code>max-width: 480px<\/code> ko\u015fullar\u0131 i\u00e7in farkl\u0131 stiller tan\u0131mlayarak, web sayfas\u0131n\u0131n daha k\u00fc\u00e7\u00fck ekranlarda daha okunakl\u0131 ve kullan\u0131labilir olmas\u0131n\u0131 sa\u011flar. \u00d6rne\u011fin, k\u00fc\u00e7\u00fck ekranlarda kenar bo\u015fluklar\u0131 azal\u0131r, ba\u015fl\u0131k font boyutlar\u0131 k\u00fc\u00e7\u00fcl\u00fcr ve d\u00fc\u011fmeler tam geni\u015flikte g\u00f6r\u00fcnt\u00fclenir.<\/p>\n<p>Bu prensipleri uygulayarak ve Qwen3.5’i g\u00fc\u00e7l\u00fc bir arka u\u00e7 API’si olarak kullanarak, uygulamalar\u0131n\u0131z\u0131n hem masa\u00fcst\u00fc hem de mobil kullan\u0131c\u0131lar i\u00e7in sorunsuz ve etkili bir deneyim sunmas\u0131n\u0131 sa\u011flayabilirsiniz. Mobil uyumluluk, kullan\u0131c\u0131 eri\u015fimini ve uygulaman\u0131z\u0131n genel ba\u015far\u0131s\u0131n\u0131 do\u011frudan etkileyen bir fakt\u00f6rd\u00fcr.<\/p>\n<h2>Sonu\u00e7: Qwen3.5 ile Gelece\u011fin \u00c7ok Modlu Uygulamalar\u0131n\u0131 \u015eekillendirmek<\/h2>\n<p>Qwen3.5 Vision-Language Modelleri ile tan\u0131\u015ft\u0131\u011f\u0131m\u0131z bu kapsaml\u0131 rehberde, \u00e7ok modlu yapay zekan\u0131n neden g\u00fcn\u00fcm\u00fcz\u00fcn ve gelece\u011fin teknolojisi oldu\u011funu, Qwen3.5’in nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 ve geli\u015ftirme ortam\u0131n\u0131n nas\u0131l kurulaca\u011f\u0131n\u0131 ad\u0131m ad\u0131m \u00f6\u011frendik. G\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama ve g\u00f6rsel soru cevaplama gibi temel uygulamalarla modelin yeteneklerini deneyimledik ve ger\u00e7ek d\u00fcnya senaryolar\u0131nda nas\u0131l kullan\u0131labilece\u011fine dair fikirler edindik. Ayr\u0131ca, ileri d\u00fczey kullan\u0131m senaryolar\u0131na, performans optimizasyon ipu\u00e7lar\u0131na ve mobil uyumlulu\u011fun \u00f6nemine de\u011findik.<\/p>\n<p>Qwen3.5, sadece bir g\u00f6rseldeki nesneleri tan\u0131makla kalmay\u0131p, bu nesneler aras\u0131ndaki ili\u015fkileri, g\u00f6rselin genel ba\u011flam\u0131n\u0131 ve metinsel sorgularla olan etkile\u015fimini de anlayabilen g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Bu yetenek, sa\u011fl\u0131k, perakende, e\u011fitim, otomasyon ve g\u00fcvenlik gibi bir\u00e7ok sekt\u00f6rde devrim niteli\u011finde uygulamalar\u0131n geli\u015ftirilmesine olanak tan\u0131r. \u0130ster eri\u015filebilirlik \u00e7\u00f6z\u00fcmleri, ister otomatik i\u00e7erik olu\u015fturma, isterse karma\u015f\u0131k veri analizi olsun, Qwen3.5 yarat\u0131c\u0131l\u0131\u011f\u0131n\u0131zla birle\u015fti\u011finde s\u0131n\u0131rs\u0131z potansiyel sunar.<\/p>\n<p>Unutmay\u0131n ki yapay zeka d\u00fcnyas\u0131 s\u00fcrekli geli\u015fiyor ve Qwen3.5 gibi modeller bu geli\u015fimin \u00f6n saflar\u0131nda yer al\u0131yor. Bu makaledeki bilgilerle donanarak, kendi yenilik\u00e7i projelerinizi hayata ge\u00e7irebilir ve \u00e7ok modlu yapay zekan\u0131n gelece\u011fine katk\u0131da bulunabilirsiniz. \u015eimdi s\u0131ra sizde: Qwen3.5’i ke\u015ffetmeye ba\u015flay\u0131n ve hayal g\u00fcc\u00fcn\u00fcz\u00fc kod sat\u0131rlar\u0131na d\u00f6k\u00fcn!<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<dl>\n<dt><strong>Qwen3.5 nedir ve di\u011fer yapay zeka modellerinden fark\u0131 nedir?<\/strong><\/dt>\n<dd>Qwen3.5, Alibaba Cloud taraf\u0131ndan geli\u015ftirilen bir Vision-Language Model (VLM) olup, hem g\u00f6rsel (resim, video) hem de metinsel verileri ayn\u0131 anda anlayabilen ve i\u015fleyebilen \u00e7ok modlu bir yapay zeka modelidir. Di\u011fer modellerden fark\u0131, sadece tek bir veri t\u00fcr\u00fcne odaklanmak yerine, iki farkl\u0131 modalite aras\u0131ndaki karma\u015f\u0131k ili\u015fkileri \u00f6\u011frenerek daha zengin ve ba\u011flama duyarl\u0131 \u00e7\u0131kt\u0131lar \u00fcretebilmesidir.<\/dd>\n<dt><strong>Qwen3.5’i kullanmak i\u00e7in hangi programlama dillerini bilmem gerekiyor?<\/strong><\/dt>\n<dd>Qwen3.5 ve benzeri \u00e7o\u011fu yapay zeka modelini kullanmak i\u00e7in temel olarak Python programlama dilini bilmeniz yeterlidir. Python’\u0131n zengin k\u00fct\u00fcphane ekosistemi (Transformers, PyTorch, PIL vb.) Qwen3.5 ile etkile\u015fim kurmay\u0131 olduk\u00e7a kolayla\u015ft\u0131r\u0131r.<\/dd>\n<dt><strong>Qwen3.5 modeli yerel bilgisayar\u0131mda \u00e7al\u0131\u015fabilir mi, yoksa bulut tabanl\u0131 bir servis mi gerektirir?<\/strong><\/dt>\n<dd>Qwen3.5 gibi b\u00fcy\u00fck modeller genellikle \u00f6nemli i\u015flem g\u00fcc\u00fc (\u00f6zellikle GPU) gerektirir. K\u00fc\u00e7\u00fck versiyonlar\u0131 veya s\u0131k\u0131\u015ft\u0131r\u0131lm\u0131\u015f (quantized) halleri yerel bilgisayar\u0131n\u0131zda (yeterli donan\u0131m varsa) \u00e7al\u0131\u015fabilirken, tam boyutlu modeller i\u00e7in genellikle bulut tabanl\u0131 GPU kaynaklar\u0131 veya API servisleri tercih edilir. Bu, hem performans hem de maliyet a\u00e7\u0131s\u0131ndan daha verimli olabilir.<\/dd>\n<dt><strong>Qwen3.5 ile hangi t\u00fcr uygulamalar geli\u015ftirebilirim?<\/strong><\/dt>\n<dd>Qwen3.5 ile \u00e7ok \u00e7e\u015fitli uygulamalar geli\u015ftirebilirsiniz: otomatik g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama, g\u00f6rsel soru cevaplama (VQA), e-ticaret i\u00e7in \u00fcr\u00fcn a\u00e7\u0131klamalar\u0131 olu\u015fturma, belge analizi, g\u00fcvenlik kameralar\u0131ndan olay tespiti, g\u00f6rme engelliler i\u00e7in eri\u015filebilirlik ara\u00e7lar\u0131, robotik ve otonom sistemlerde \u00e7evre anlama gibi bir\u00e7ok \u00e7ok modlu yapay zeka uygulamas\u0131n\u0131 hayata ge\u00e7irebilirsiniz.<\/dd>\n<dt><strong>Qwen3.5 modelini \u00f6zelle\u015ftirebilir miyim?<\/strong><\/dt>\n<dd>Evet, Qwen3.5 gibi \u00f6nceden e\u011fitilmi\u015f modelleri kendi \u00f6zel veri setlerinizle ince ayar (fine-tuning) yaparak belirli g\u00f6revler veya alanlar i\u00e7in \u00f6zelle\u015ftirebilirsiniz. Bu s\u00fcre\u00e7, modelin mevcut bilgilerini korurken, yeni verilerden \u00f6\u011frenerek performans\u0131n\u0131 belirli bir alanda daha da art\u0131rmas\u0131n\u0131 sa\u011flar. Ancak ince ayar yapmak, ek veri setleri ve hesaplama kaynaklar\u0131 gerektirebilir.<\/dd>\n<\/dl>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"Qwen3.5 Vision-Language Modellerine Ba\u015flang\u0131\u00e7 Rehberi G\u00f6rsel ve metinsel verileri ayn\u0131 anda i\u015fleyebilen yapay zeka modelleri, g\u00fcn\u00fcm\u00fcz teknolojisinde \u00e7\u0131\u011f\u0131r…","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-41199","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>\u00c7ok Modlu Yapay Zeka ile Gelece\u011fe Ad\u0131m Atmak: Neden Qwen3.5?<\/title>\n<meta name=\"description\" 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