The HyperFit Python package is designed to fit N-dimensional data with an N-1 dimensional plane. It can account for uncertainties in both the x and y data, with the ability for these uncertainties to be covariant. The model assumes that the data is Gaussian-distributed about this plane. Based on the R HyperFit package by Aaron Robotham and Danail Obreschkow (https://ui.adsabs.harvard.edu/abs/2015PASA...32...33R/abstract), the Python version available at https://github.com/cullanhowlett/HyperFit was written by Cullan Howlett. Here, we share a modified version of HyperFit, with the addition of the MultiLinFit class. MultiLinFit is limited to only fitting 2-dimensional data with a line, but it has the ability to fit multiple data sets simultaneously and assumes that all data sets share a common slope but have different intercepts and (potentially) different measures of intrinsic Gaussian scatter around their lines. This package is used in the Tully-Fisher Relation analysis of the DESI Peculiar Velocity Survey: Early Data Release: "DESI EDR: Calibrating the Tully-Fisher Relationship with the DESI Peculiar Velocity Survey" by Kelly Douglass et al. Year 1: "The DESI DR1 Peculiar Velocity Survey: The Tully-Fisher Distance Catalog" by Kelly Douglass et al.