This paper presents a Hybrid Internal Model (HIM) based method for legged locomotion control in quadruped robots. The method aims to address the limitations of existing learning-based locomotion control paradigms, which suffer from information losses, noisy observations, sample efficiency, and difficulties in developing general locomotion policies for robots with different sensor configurations. The proposed HIM method leverages joint encoders and an Inertial Measurement Unit (IMU) as the only sensors for predicting robot states. Considering the prediction frequency is higher than 50 Hz, the method infers current robot states upon the previous trajectory. The framework consists of two components: the information extractor HIM and the policy network. Unlike previous methods that explicitly model environmental observations such as base velocity and ground elevation, HIM only explicitly estimates velocity and encodes other environment dynamics as an implicit latent embedding. The latent dynamics are learned through contrastive learning, which enhances robustness and adaptability in disturbed and unpredictable environments. The proposed method is validated through simulations in different terrains and real-world experiments on the Unitree Go1 robot. The results demonstrate that HIM achieves substantial agility over challenging terrains with minimal sensors and fast convergence. The method shows promise for broader applications in locomotion control.