The article investigates various design choices in vision-language models (VLMs) through controlled experiments, focusing on architecture effectiveness, inference cost trade-offs, and training stability. The result is the Idefics2 model, which comprises 8 billion parameters and has achieved state-of-the-art results across multiple benchmarks in its category. The paper emphasizes the model's efficiency during inference while aiming to alleviate complex real-world challenges through the open release of findings, models, and training datasets. Additionally, it includes acknowledgements for contributions to the research and exploration of model evaluations and improvements.
This work rigorously compares common design choices in vision-language models, shedding light on architecture effectiveness, efficiency, and training stability, concluding with Idefics2's superior performance.
We aim to contribute to the evolution of vision-language models by releasing our findings, models, and training datasets to address complex real-world problems with our state-of-the-art Idefics2.
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