Neural music instrument cloning is an application of deep neural networks for imitating the timbre of a particular music instrument recording with a trained neural network. One can create such clones using an approach such as DDSP [1], which has been shown to achieve good synthesis quality for several instrument types [2]. However, this approach needs about ten minutes of audio data from the instrument of interest (target recording audio). In this work, we modify the DDSP architecture and apply transfer learning techniques used in speech voice cloning [3] to significantly reduce the amount of target recording audio required. We compare various cloning approaches and architectures across durations of target recording audio, ranging from four to 256 seconds. We demonstrate editing of loudness and pitch as well as timbre transfer from only 16 seconds of target recording audio. Our code is available online1 as well as many audio examples.2