L2R-VTC stands for Large-Scale Language Model fine-tuned using transfer learning techniques to achieve state-of-the-art performance in specific Vietnamese language tasks. These models have become a cornerstone of modern Vietnamese natural language processing (NLP), enabling advancements in areas such as text classification, entity recognition, and question answering. The development of L2R-VTC models is particularly significant given Vietnam's rapid technological evolution. By leveraging pre-trained large language models (LLMs) from multilingual settings, Vietnamese researchers can bridge gaps between global AI advancements and local linguistic nuances. This approach ensures that models not only understand but also generate high-quality Vietnamese text in various contexts. One of the key advantages of L2R-VTC is its scalability. These models can be fine-tuned to accommodate diverse tasks, from customer service chatbots to specialized domain applications. This adaptability has led to widespread adoption across industries, including education, healthcare, and finance, where precise language understanding is crucial. Moreover, the deployment of L2R-VTC in production environments has been made smoother due to advancements in computational efficiency and model compression techniques. Vietnamese tech companies are now capable of integrating these models into real-world applications, driving innovation and efficiency in their products and services. Looking ahead, the integration of L2R-VTC with other AI technologies such as reinforcement learning and generative models presents exciting opportunities. These models could further enhance Vietnam's NLP capabilities, enabling more sophisticated applications that cater to both global standards and local needs. In conclusion, the rise of L2R-VTC represents a transformative leap in Vietnamese AI development. By harnessing the power of transfer learning and large language models, Vietnam is poised to lead in creating AI solutions tailored to its unique linguistic and cultural contexts.