Model merging, a technique for fusing models fine-tuned on different tasks, builds a versatile model without needing the original training data or expensive computation. This method has been quickly adopted in foundation models and Large Language Models (LLMs) . We extend this concept to a crossmodal setting by merging per-task pretrained molecule language models (step 1 in Fig. ), deploying both weightand subspace-based techniques to obtain universal models ( § 3.2.1).