What is nlp-remove-jspace-layer?

nlp-remove-jspace-layer is an interpretability experiment that looks at what happens inside Llama-3.1-8B-Instruct, a large language model, when one of its internal processing layers is removed. Language models like this one are built from many stacked layers that each pass along and adjust a running signal, and this project studies which of those layers actually matter for turning a prompt into meaningful output. Using a visualization technique called t-SNE, the authors map how the model's internal representations of different topics change as they pass through each layer. Early layers keep topics cleanly separated, since they capture surface level, token by token patterns. In the middle of the network the topic categories blur together and overlap, which the authors interpret as the layers where the model is actually forming meaning rather than just tracking words. In the later layers the categories separate again into clean, task focused groupings. The core experiment removes one of those middle, meaning forming layers while keeping the rest of the network running normally, using a reversible trick that lets the skipped layer's input pass straight through unchanged. The project then compares text the model generates normally against text generated with that layer skipped, measuring how far the predictions shift and how often the model's top guess for the next word changes. Running it requires a GPU machine to host the 8 billion parameter model through a small local web server, while a separate lightweight interface, meant to run on a Mac, connects to that server over an SSH tunnel to send prompts and view side by side comparisons of the baseline and layer removed outputs, along with the measured differences. A command line tool is also included for scripting comparisons across a range of layers and plotting the results. This is a small scale research project aimed at people already comfortable with language model internals, rather than a general purpose tool.