In recent research, significant advancements have been achieved in tool learning for large language models. Looking towards future advanced studies, the issue of fully autonomous tool utilization is particularly intriguing: given only a query, language models can autonomously decide whether to employ a tool, which specific tool to select, and how to utilize these tools, all without needing any tool-specific prompts within the context. To achieve this, we introduce a unified, efficient, and scalable framework for fine-tuning language models. Based on the degree of tool dependency, we initially categorize queries into three distinct types. By transforming the entire process into a sequential decision-making problem through conditional probability decomposition, our approach unifies the three types and autoregressively generates decision processes. Concurrently, we’ve introduced an “instruct, execute, and reformat” strategy specifically designed for efficient data annotation. Through end-to-end training on the annotated dataset comprising 26 diverse APIs, the model demonstrates a level of self-awareness, automatically seeking tool assistance when necessary. It significantly surpasses original instruction-tuned open-source language models and GPT-3.5/4 on multiple evaluation metrics. To address real-world scalability needs, we’ve enhanced our framework with a dynamic rehearsal strategy for continual learning, proven to require minimal new annotations to exhibit remarkable performance.