Show HN: LLM Attention Visualization (ishamf.dev)

A new tool showcasing LLM (Large Language Model) attention visualization has been announced, enabling users to interactively explore how previous tokens influence the generation of new tokens in transformer-based language models. By hovering over generated tokens, users can see which past tokens carried the highest "attention weight," effectively illustrating the model's selection mechanism when creating text. This visualization reveals notable patterns in the model's behavior, such as the ability to accurately copy specific phrases while maintaining context, contradicting the assumption that probabilistic prediction leads to higher error rates. Significantly, this tool enhances our understanding of LLMs by visualizing the attention process, highlighting how models can selectively draw on substantial past data during text generation. Technical details include using a React app with Transformers.js for text generation, while addressing challenges in accessing and visualizing internal model data. The project also required modifying the ONNX model for better visibility of non-output values and implementing a pre-generated prompt system to enhance user experience. This new tool offers valuable insights into the mechanics of LLMs, fostering a deeper comprehension of their capabilities and limitations in natural language processing.