In this paper, we unveil that Language Models (LMs)  can acquire new capabilities by assimilating parameters from homologous models without retraining or GPUs. We first introduce DARE to set most delta parameters (i.e., the disparity between fine-tuned and pre-trained parameters)  to zeros without affecting the abilities of Supervised Fine-Tuning (SFT)  LMs, which randomlyDrops delta parameters with a ratioAndREscales the remaining ones byto approximate the original embeddings. Then, we use DARE as a versatile plug-and-play technique to sparsify delta parameters of multiple SFT homologous models for mitigating parameter interference and merge them into a single model by parameter fusing. We experiment with encoder- and decoder-based LMs, showing that: (1)  SFT delta parameter value ranges are typically small (within 0.005)  with extreme redundancy, and DARE can effortlessly eliminate 90% or even 99% of them. (2)  DARE can merge multiple task-specific LMs into one LM with diverse capabilities. For instance, the amalgamation of WizardLM and WizardMath significantly enhances the GSM8K zero-shot accuracy of WizardLM from 2.2 to 66.3, retaining the instruction-following proficiency while surpassing WizardMath’s 64.2 performance. Our merged LM also ranks first among models with 7 billion parameters on the Open LLM Leaderboard.