Video of how my LLM's decoder blocks changed while training

AI researcher CurvedInf has created a compelling video visualization that shows the dynamic evolution of decoder blocks within a large language model during training. The visualization tracks how attention heads and feed-forward networks transform across training steps, revealing the internal mechanics of models like LLaMA as they learn. Originally shared on Reddit's r/LocalLLaMA community, the content went viral for providing an unprecedented look at what happens inside neural networks as they develop capabilities.

The visualization reveals several key patterns: attention heads initially show random activation patterns that gradually organize into specialized functions, while feed-forward networks develop more structured weight distributions over time. Researchers can observe how different components of the model specialize for specific tasks throughout the training process. The video has sparked discussions about training dynamics and could influence future approaches to model architecture design and optimization.

CurvedInf noted that Reddit's compression affected video quality and provided an alternative link on X (formerly Twitter) for better viewing. The visualization represents a significant contribution to interpretability research, making abstract training concepts more accessible to both researchers and enthusiasts. By showing the 'learning process' of AI models in visual form, it bridges the gap between theoretical understanding and practical observation of neural network development.