Bonito is an open-source model for conditional task generation that converts unannotated text into task-specific training datasets for instruction tuning. It is built on top of Hugging Face transformers and vLLM libraries, using LLM inference to synthesize training data from raw text. The generated datasets enable fine-tuning other language models for zero-shot task adaptation without manual annotation.