The scientific manuscript review stage is a key part of the modern scientific process. It involves rigorous evaluation of new papers by peers to assess the significance of contributions in a particular area of study and ensure that papers meet high standards. This process helps maintain the quality and credibility of research. However, some reviews can be toxic or overly discouraging, leading to unintentional psychological damage (such as anxiety or depression) to paper authors and detracting from the constructive tone of the review space. This Thesis/Creative Project was completed alongside a capstone project. Our capstone project aims to address this issue. The goal is to fine tune a Large Language Model (LLM) that can first accurately identify toxic sentences within a paper review. Then, the LLM will revise any toxic sentences in a way that maintains the criticism but delivers it in a more friendly or encouraging tone. To effectively use this LLM, it requires a Graphical User Interface (GUI) so that end-users (such as editors, associate editors, reviewers) can easily interact with it. This allows them to update the wording of the review in an effective manner while maintaining scientific integrity. While the GUI provides a user-friendly interface for interacting with the LLM, there are some technical challenges in running a LLM application in a web-based framework. LLMs are computationally expensive to run. They require significant GPU RAM, which can be a limiting factor, especially in a web-based framework with limited resources. One potential solution to this problem is model quantization, which can reduce the memory footprint of the model. However, this introduces the problem of model drift, as the model’s performance may decrease when quantized. This needs to be measured to ensure the model continues to provide accurate results.