Title: Exploring and Evaluating Personalized Models for Code Generation. Abstract: Large Transformer models achieved the state-of-the-art status for Natural Language Understanding and are increasingly the baseline architecture for source code generation models. Transformers are usually pre-trained on a large unsupervised corpus, learning token representations and transformations relevant to modeling generally available text, and then fine-tuned on a particular task of interest. While fine-tuning is a tried-and-true method for adapting a model to a new domain, for example question-answering on a given topic or a source code generation model, generalization remains an on-going challenge. Here we explore the ability of various levels of model fine-tuning to improve generalization by personalized fine-tuning. In the context of generating unit tests for Java methods, here we evaluate learning to personalize to a specific project using several methods to personalize transformer models for unit test generation for a specific Java project. We consider three fine-tuning approaches: (i) custom fine-tuning, which allows all the model parameters to be tuned; (ii) lightweight fine-tuning, which freezes most of the model's parameters, allowing a tuning of the token embeddings and softmax layer or the final layer alone; (iii) prefix tuning, which keeps language model parameters frozen, but optimizes a small project-specific prefix vector. Each of these techniques offers a different trade-off in total compute cost and prediction performance, which we evaluate by code and task-specific metrics, training time, and total computational operations. We compare these fine-tuning strategies for code generation and discuss the potential generalization and cost benefits of each in deployment scenarios.