Running Qwen 3.8 27B locally to interpret neural network model weights

Simon Willison explores using the locally run Qwen 3.8 27B model to interpret and reason about neural network model weights directly. This experiment investigates whether current large language models possess the capability to understand complex weight structures. This capability touches on the emerging concept of weight-space programming, where LLMs could potentially read and modify neural network weights to find bugs or optimize performance. It bridges the gap between traditional training cycles and direct, human-like reasoning over machine learning parameters. The exploration utilizes Qwen 3.8 27B, a distilled model from Alibaba's Qwen family that runs locally. It opens up questions about whether models can accurately interpret their own weights or the weights of smaller models like Qwen 3.5 0.8B.