  Large Language Models (LLMs) have demonstrated remarkable progress in
utilizing tools, but their closed-source nature and high inference costs pose
limitations on their adaptability, necessitating a valid method that leverages
smaller, open-sourced models. In this paper, we introduce Toolink, a
comprehensive framework that performs task-solving by first creating a toolkit
and then integrating the planning and calling of tools through a
chain-of-solving (CoS) approach. We first validate the efficacy of Toolink in
harnessing the model's creativity and CoS ability on ChatGPT. Subsequently, we
curate CoS-GPT, a chain-of-solving dataset designed for tool-using, and
finetune the LLaMA-7B model. It results in LLaMA-CoS, a powerful open-source
model with advanced tool-planning and tool-calling capabilities. Evaluation of
diverse tasks from BIG-bench demonstrates its CoS ability matches that of
ChatGPT while its performance surpasses the chain-of-thought approach. Further
studies highlight the generalization of LLaMA-CoS to unseen tasks and showcase
its capability in using toolkits not explicitly tailored for the target task,
affirming its robustness in real-world scenarios.
