  Astrophysical explorations are underpinned by large-scale stellar
spectroscopy surveys, necessitating a paradigm shift in spectral fitting
techniques. Our study proposes three enhancements to transcend the limitations
of the current spectral emulation models. We implement an attention-based
emulator, adept at unveiling long-range information between wavelength pixels.
We leverage a domain-specific fine-tuning strategy where the model is
pre-trained on spectra with fixed stellar parameters and variable elemental
abundances, followed by fine-tuning on the entire domain. Moreover, by treating
wavelength as an autonomous model parameter, akin to neural radiance fields,
the model can generate spectra on any wavelength grid. In the case with a
training set of O(1000), our approach exceeds current leading methods by a
factor of 5-10 across all metrics.
