In recent years, the rapid expansion of transformer-based architectures has significantly advanced the capabilities of text generation, translation, and other language-centric tasks. However, scaling such models while maintaining task-specific adaptability has become increasingly challenging due to the growing complexity of architectures and computational demands. Token adapters, a novel mechanism explored in this research, present a modular solution to extending model functionality without requiring extensive retraining of the core architecture. By introducing task-specific token manipulation layers into the Mistral model, substantial improvements were observed in perplexity, BLEU scores, and overall accuracy across a variety of tasks. The experimental results demonstrated the effectiveness of token adapters in enhancing linguistic coherence and context sensitivity, particularly for machine translation and question answering, while also highlighting the trade-offs in terms of memory consumption and training time. This research contributes to the broader exploration of model extensibility and proposes token adapters as a promising avenue for task-specific optimization within scalable architectures. The findings demonstrate the potential for modular approaches to increase both flexibility and specialization in language models, positioning token adapters as a valuable tool for future innovations in the field.