To accurately interpret the observed properties of exoplanets, it is necessary to first obtain a detailed understanding of host star properties. However, physical models that analyze stellar properties on a per-star basis can become computationally intractable for sufficiently large samples. Furthermore, these models are limited by the wavelength coverage of available spectra. We combine previously derived spectral properties from the Spectroscopic Properties of Cool Stars (SPOCS) catalog (Brewer et al. 2016) with generative modeling using The Cannon to produce a model capable of deriving stellar parameters ($log g$, $T_{mathrm{eff}}$, and $vsin i$) and 15 elemental abundances (C, N, O, Na, Mg, Al, Si, Ca, Ti, V, Cr, Mn, Fe, Ni, and Y) for stellar spectra observed with Keck Observatorys High Resolution Echelle Spectrometer (HIRES). We demonstrate the high accuracy and precision of our model, which takes just $sim$3 seconds to classify each star, through cross-validation with pre-labeled spectra from the SPOCS sample. Our trained model, which takes continuum-normalized template spectra as its inputs, is publicly available at https://github.com/malenarice/keckspec. Finally, we interpolate our spectra and employ the same modeling scheme to recover labels for 477 stars using archival stellar spectra obtained prior to Kecks 2004 detector upgrade, demonstrating that our interpolated model can successfully predict stellar labels for different spectrographs that have (1) sufficiently similar systematics and (2) a wavelength range that substantially overlaps with that of the post-2004 HIRES spectra.
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