Unit tests serve a critical role in software development in ensuring quality and pre-dictability for components of code. While unit tests are important, they are a resourceheavy part of the development life cycle. Hence, the automation of unit tests is an inten-sive research area. This paper looks at the use of Large Language Models (LLMs) to achievethis task by looking at both fine-tuning and in-context learning of various sizes in order toautomatically generate unit tests given a function, and seeing how they perform in terms ofa similarity score. In order to achieve this, this thesis proposes an implementation pipelinewhich consist of creating a dataset that maps functions to unit tests, training an LLM, Wiz-ardCoder, of various sizes on a GPU-cluster, running inference, and evaluating the qualityof the generated tests. These results that the fine-tuned models consistently outperformthe models using in-context learning, independent of size. Moreover, within the scope offine-tuned models, the models using more parameters performed slightly better than theirsmaller counterparts.