Modeling and control of the human neuro-musculo-skeletal system is important for understanding human movement, developing embodied intelligence, and optimizing human-robot interaction. However, current models are restricted to a limited range of body parts and often with a reduced number of muscles. There is also a lack of algorithms capable of controlling over 600 muscles to generate reasonable human movements. To fill this gap, we build a musculoskeletal model with all of the body segments, joints, and muscle-tendon units, allowing simulation of whole-body dynamics and interaction with various devices. We develop a new algorithm using low-dimensional representation and hierarchical deep reinforcement learning to achieve state-of-the-art whole-body control. We validate the effectiveness of our model and algorithm in simulations using real human locomotion data. This work promotes a deeper understanding of human motion control and better design of interactive robots.