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Large Language Models (LLMs) are pivotal for advanced text processing and generation. This study presents a framework to train a series of on-device LLMs optimized for invoking software APIs. Using a curated dataset of 30,000 API function calls from software documentation, we fine-tune LLMs with 2B, 3B, and 7B parameters to enhance their proficiency in API interactions. Our approach improves the understanding of API structures and syntax, leading to significantly better accuracy in API function calls. We also propose a conditional masking technique to enforce correct output formats, reducing errors while maintaining inference speed, specifically tailored for API tasks. The fine-tuned model, Octopus, outperforms GPT-4 in API calling tasks, showcasing advancements in automated software development and API integration. The model checkpoints are publicly available.