Magnetic resonance fingerprinting (MRF) simultaneously quantifies multiple tissue properties. Deep learning accelerates MRF’s tissue mapping time; however, previous deep learning MRF models are supervised, requiring ground truths of tissue property maps. It is challenging to acquire quality reference maps, especially as the number of tissue parameters increases. We propose a self-supervised model informed by the physical model of the MRF acquisition without requiring ground truth references. Additionally, we construct a forward model that directly estimates the gradients of the Bloch equations. This approach is flexible for modeling MRF sequences with pseudo-randomized sequence designs where an analytical model is not available.