ALLaVA is a synthetic dataset designed to enhance the performance of lightweight vision-language models (LVLMs) by leveraging high-quality data generated through a Caption-then-QA pipeline. The dataset, containing 1.3 million samples, includes fine-grained captions, complex instructions, and detailed answers generated using GPT-4V. It is curated from two sources: LAION and Vision-FLAN, ensuring a diverse range of images and tasks. The dataset is used to train a series of lightweight LVLMs, which achieve competitive performance on multiple benchmarks, often matching or exceeding larger models. The approach emphasizes data quality and alignment to improve the efficiency and effectiveness of LVLMs. ALLaVA is open-sourced to support research into more resource-efficient LVLMs. The methodology involves generating high-quality captions and visual question-answering pairs through a structured pipeline, ensuring comprehensive data coverage and diverse topics. The dataset's effectiveness is validated through experiments showing improved performance on various benchmarks, demonstrating its value in advancing lightweight LVLMs. Ethical considerations are also addressed, ensuring the dataset avoids biased or inappropriate content. Overall, ALLaVA provides a valuable resource for developing more efficient and effective LVLMs.ALLaVA is a synthetic dataset designed to enhance the performance of lightweight vision-language models (LVLMs) by leveraging high-quality data generated through a Caption-then-QA pipeline. The dataset, containing 1.3 million samples, includes fine-grained captions, complex instructions, and detailed answers generated using GPT-4V. It is curated from two sources: LAION and Vision-FLAN, ensuring a diverse range of images and tasks. The dataset is used to train a series of lightweight LVLMs, which achieve competitive performance on multiple benchmarks, often matching or exceeding larger models. The approach emphasizes data quality and alignment to improve the efficiency and effectiveness of LVLMs. ALLaVA is open-sourced to support research into more resource-efficient LVLMs. The methodology involves generating high-quality captions and visual question-answering pairs through a structured pipeline, ensuring comprehensive data coverage and diverse topics. The dataset's effectiveness is validated through experiments showing improved performance on various benchmarks, demonstrating its value in advancing lightweight LVLMs. Ethical considerations are also addressed, ensuring the dataset avoids biased or inappropriate content. Overall, ALLaVA provides a valuable resource for developing more efficient and effective LVLMs.