Cosmos-LLaVA: Chatting with the Visual Cosmos-LLaVA: G"orselle Sohbet Etmek

In this study, a Turkish visual instruction model was developed and various model architectures and dataset combinations were analysed to improve the performance of this model. The Cosmos-LLaVA model, which is built by combining different large language models and image coders, is designed to overcome the deficiencies in the Turkish language. In the experiments, the effects of fine-tuning with various datasets on the model performance are analysed in detail. The results show that model architecture and dataset selection have a significant impact on performance. Bu ccalicsmada bir T"urkcce g"orsel talimat modeli gelicstirilerek bu modelin performansini artirmaya y"onelik ccecsitli model mimarileri ve veri k"umesi kombinasyonlari derinlemesine incelenmicstir. Farkli b"uy"uk dil modelleri ve g"or"unt"u kodlayicilarinin bir araya getirilmesiyle olucsturulan Cosmos-LLaVA modeli, T"urkcce dilindeki eksiklikleri gidermeye y"onelik olarak tasarlanmicstir. Yapilan deneylerde, ccecsitli veri k"umeleri ile yapilan ince ayarlarin model performansini nasil etkilediugi detayli olarak ele alinmicstir. Sonucclar, model mimarisi ve veri k"umesi secciminin performans "uzerinde "onemli bir etkiye sahip olduugunu g"ostermektedir.