# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Examples of Prominent MLLMs
#### 2.4.1 Open-Source MLLMs
NVLM 1.0's public release is a significant contribution to the open-source ecosystem, offering powerful tools for research and development in multimodal AI. Its focus on maintaining text-only performance during multimodal training sets a new standard, enabling its integration into educational technologies without compromising existing capabilities. By providing production-grade multimodality and robust benchmarks, NVLM supports the creation of advanced applications across various fields, including education, healthcare, and content generation. Potential applications: NVLM 1.0 can be utilized to build OCR-based educational tools, multimodal math reasoning assistants, and adaptive content generation systems. For example, its superior OCR capabilities can support the digitization of historical documents for research purposes, while its multimodal reasoning abilities can enhance STEM education with visual problem-solving tasks. **Llama 3-V**: Llama 3-V is the latest addition to Meta's family of open-source foundation models, featuring a herd of models designed to natively support multilinguality, coding, reasoning, and tool usage. The largest variant of Llama 3-V is a dense transformer model with 405 billion parameters and a context window of up to 128 K tokens [29]. The models are trained on an unprecedented corpus of 15 trillion multilingual tokens, enabling them to achieve state-of-the-art performance across a wide range of language understanding tasks. The Llama 3-V models, available under an updated open-source license, include pre-trained and post-trained versions, with the flagship Llama 3-V.1 model delivering competitive quality compared to GPT-4. One of the major advantages of Llama 3-V lies in its versatility and performance. By supporting multilinguality and tool usage, it offers immense flexibility for global educational applications, including language instruction, cross-cultural studies, and adaptive learning environments. Its extended context window makes it particularly well suited for tasks requiring in-depth reasoning, such as literature analysis, historical document interpretation, and coding exercises. Additionally, Meta's emphasis on safety, demonstrated through the Llama Guard 3 model for input and output safety, ensures that the models can be deployed responsibly in educational contexts. Despite its strengths, Llama 3-V presents challenges. While being open-source, its large-scale models like the 405B parameter variant require substantial computational resources, making them difficult to deploy for institutions with limited infrastructure. The extensive training and fine-tuning processes, although yielding exceptional results, also necessitate expertise and investment in hardware and personnel. Moreover, while Meta has improved pre-training data quality and post-training alignment processes, the scale of Llama 3-V may raise concerns about data privacy and ethical implications in specific use cases. Llama 3-V's compositional approach to multimodality--integrating image, video, and speech recognition capabilities--shows promising results that rival state-of-the-art models in these domains. Although these multimodal extensions are still under development, they highlight the potential of Llama 3-V to transform education by enabling innovative applications such as visual storytelling, multilingual video-based lessons, and speech-assisted learning tools. The commitment to public release ensures transparency and fosters a collaborative research ecosystem to enhance the model further. Overall, Llama 3-V represents a significant milestone in the evolution of open-source MLLMs, demonstrating the potential of large-scale, multimodal models to advance global education and research. Its balance between performance, openness, and safety positions it as a valuable asset for institutions aiming to integrate cutting-edge AI technologies into their educational strategies. Potential applications: Llama 3-V can enable visual storytelling, multilingual video-based lessons, and adaptive learning platforms.