The release of GPT-4o, a multimodal model that combines vision, audio, and text, has marked a significant milestone in the field of AI. This breakthrough has paved the way for advanced research into creating models that can handle and integrate multiple types of data inputs. Following this development, we have embarked on a research journey to achieve similar multimodal capabilities with LLaMA3.

Our research focuses on extending the LLaMA3 architecture to process and integrate text, audio, and video inputs seamlessly. We aim to leverage the insights gained from GPT-4o's multimodal capabilities and apply them to our model, ensuring that it can understand and generate outputs across various formats. This involves:

• Data Fusion Techniques: Researching methods to effectively combine and interpret data from different modalities, ensuring that the model can process complex inputs and generate coherent outputs.
• Cross-Modal Attention Mechanisms: Implementing attention mechanisms that allow the model to focus on relevant parts of each modality, improving its ability to understand and integrate diverse data sources.
• Multimodal Embeddings: Developing embeddings that represent the combined information from text, audio, and video, enabling the model to utilize this integrated knowledge for more accurate and contextually aware responses.