The fundamental language of the brain is articulated by timing and frequency patterns of neuronal output that are interpreted by receiving neurons through a basic mechanism known as short-term plasticity (STP). However, these synaptic dynamics exhibit considerable heterogeneity both within and across genetically defined cell types which poses a critical barrier to interpreting this language. Beninger et al. addressed this heterogeneity in excitatory human and rodent synapses by organizing their dynamics into functional subtypes which cannot be explained by continuum hypotheses and partly overlap genetic cell identities. We provide an account of the other major class of synaptic dynamics: inhibitory (GABAergic) dynamics. Using a combination of machine learning and synaptic modeling on the largest existing pair-patch clamp synaptic dynamics dataset we show that the dynamics of inhibitory connections organize into a functional continuum with distinct but overlapping modes corresponding to different genetic identities. First, we fit flexible synaptic models to the Allen Institute Synaptic Physiology Dataset resulting in a low-dimensional representation of synaptic dynamics. We then tested the degree to which functional dynamics are genetically structured by applying supervised machine learning to predict presynaptic genetic identities from fitted parameters. Testing yielded predictive accuracies above baseline, demonstrating an alignment with these identities. Interestingly, unlike in excitatory synapses, unsupervised machine learning did not yield clusters that were of obviously different quality from clustering random noise with the same variance structure. Instead, both our model parameters and supervised prediction weights are organized into a relatively smooth, overlapped continuum between Parvalbumin (Pvalb) and Vasoactive Intestinal Peptide (VIP) expressing neurons with Somatostatin (Sst) expressing neurons acting as a bridging, transitional class. Paired with excitatory functional subtypes this inhibitory continuum completes a functional picture of canonical cortical circuit motifs and how their synaptic dynamics influence information processing.