In the task of music style transfer, the symbolic music representation based on Musical Instrument Digital Interface (MIDI) files has always been a popular research medium. By using such representation, some mature models for image style transfer can also be applied to this scenario, such as Cycle-consistent Generative Adversarial Networks (CycleGAN). However, this MIDI-based data representation is not suitable for guitar music because it does not support unique expressive information of guitar playing, such as bending, sliding, or other playing techniques. DadaGP, a dataset made up of guitar-specific format files (tablatures) and their rendered text-like tokens, enables us to perform symbolic guitar music style transfer leveraging expressive guitar playing information, and to produce playable guitar tablatures. We first adopt K-hot encoding to transform the task from sequence generation to binary classification of multiple variables, and use top-$k$ sampling to reproduce sequences from output K-hot vectors. We then propose a novel model we call CycleGMT, a CycleGAN-based model for symbolic guitar music style transfer. Finally, to mitigate the severe sparsity in the data and its resulting content loss, we adopt a skip connection between the input and output of the model, successfully achieving style-transferred music whose quality being competitive with human-composed remixes, while the musical complexity of the style-transferred music can be controlled by adjusting the value of $k$ in top-$k$ sampling.