The logit lens is a standard mechanistic interpretability tool for inspecting a Transformer’s predictions across layers. However, existing logit lens tools typically focus on individual checkpoints rather than interactive exploration of a model’s *training history*.

In this study, we introduce **LensLapse**, an open-source web application for interactive exploration of a model’s training history through token-level interpretability. Users can scrub a slider across public checkpoints from five model families and three multilingual checkpoint suites and observe how per-layer, per-position logit lens predictions evolve from frequency bias to factual knowledge.

For browser-compatible models, arbitrary user prompts are probed *live in the browser* via local inference over per-checkpoint model pairs. No server, no API key, no hosting cost, and prompts never leave the user’s device.

Moreover, **LensLapse** supports local models. A single ‘pip install‘ enables users to launch the application and explore their own models and training checkpoints without any additional web setup.

LensLapse is MIT-licensed and permanently hosted at iamtatsuki05.github.io/lenslapse.

- GitHub: https://github.com/iamtatsuki05/lenslapse - PyPI: https://pypi.org/project/lenslapse/ - docs: https://iamtatsuki05.github.io/lenslapse/docs/ - YouTube: https://youtu.be/OBnvG9Ru3j8