  With the rapid development of IT operations, it has become increasingly
crucial to efficiently manage and analyze large volumes of data for practical
applications. The techniques of Natural Language Processing (NLP) have shown
remarkable capabilities for various tasks, including named entity recognition,
machine translation and dialogue systems. Recently, Large Language Models
(LLMs) have achieved significant improvements across various NLP downstream
tasks. However, there is a lack of specialized LLMs for IT operations. In this
paper, we introduce the OWL, a large language model trained on our collected
OWL-Instruct dataset with a wide range of IT-related information, where the
mixture-of-adapter strategy is proposed to improve the parameter-efficient
tuning across different domains or tasks. Furthermore, we evaluate the
performance of our OWL on the OWL-Bench established by us and open IT-related
benchmarks. OWL demonstrates superior performance results on IT tasks, which
outperforms existing models by significant margins. Moreover, we hope that the
findings of our work will provide more insights to revolutionize the techniques
of IT operations with specialized LLMs.
