  Current research on generative language models (GLMs) for automated text
scoring (ATS) has focused almost exclusively on querying proprietary models via
Application Programming Interfaces (APIs). Yet such practices raise issues
around transparency and security, and these methods offer little in the way of
efficiency or customizability. With the recent proliferation of smaller,
open-source models, there is the option to explore GLMs with computers equipped
with modest, consumer-grade hardware, that is, for the "GPU poor." In this
study, we analyze the performance and efficiency of open-source, small-scale
GLMs for ATS. Results show that GLMs can be fine-tuned to achieve adequate,
though not state-of-the-art, performance. In addition to ATS, we take small
steps towards analyzing models' capacity for generating feedback by prompting
GLMs to explain their scores. Model-generated feedback shows promise, but
requires more rigorous evaluation focused on targeted use cases.
