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This paper introduces AsyncTool, a benchmark for evaluating LLM-based agents' asynchronous function calling abilities in multi-task scenarios with delayed tool responses. It proposes efficiency-oriented metrics and identifies key failure modes of current tool-using agents.
A self-taught developer asks for advice on choosing between 3B and 7B models for a first multi-task fine-tuning project focused on deeper reasoning about underlying questions.
Florence-2 is an advanced vision foundation model from Microsoft that uses a prompt-based approach to handle various vision and vision-language tasks like captioning and object detection, trained on a large annotated dataset.