  This paper explores the utilization of LLMs for data preprocessing (DP), a
crucial step in the data mining pipeline that transforms raw data into a clean
format conducive to easy processing. Whereas the use of LLMs has sparked
interest in devising universal solutions to DP, recent initiatives in this
domain typically rely on GPT APIs, raising inevitable data breach concerns.
Unlike these approaches, we consider instruction-tuning local LLMs (7 -- 13B
models) as universal DP task solvers that operate on a local, single, and
low-priced GPU, ensuring data security and enabling further customization. We
select a collection of datasets across four representative DP tasks and
construct instruction tuning data using data configuration, knowledge
injection, and reasoning data distillation techniques tailored to DP. By tuning
Mistral-7B, Llama 3-8B, and OpenOrca-Platypus2-13B, our models, namely,
Jellyfish-7B/8B/13B, deliver competitiveness compared to GPT-3.5/4 models and
strong generalizability to unseen tasks while barely compromising the base
models' abilities in NLP tasks. Meanwhile, Jellyfish offers enhanced reasoning
capabilities compared to GPT-3.5.
  Our models are available at: https://huggingface.co/NECOUDBFM/Jellyfish .
  Our instruction dataset is available at:
https://huggingface.co/datasets/NECOUDBFM/Jellyfish-Instruct .
