Note - I'm planning for a major refactor of this library soon - branch can be found here (containing demo examples too). The current way it works is an unholy patchwork of Python / HTML / JavaScript; the new version is much simpler: the vis is created with a minimal pre-existing HTML framework, instead it's populated using JavaScript, and the only way Python interfaces with JavaScript is to dump a single DATA dictionary into the JavaScript page. I've also created an Othello SAE vis, pictured below (also see it on my personal website homepage). I plan to get around to pushing updates to this library in late September / early October, so watch this space!

This codebase was designed to replicate Anthropic's sparse autoencoder visualisations, which you can see here. The codebase provides 2 different views: a feature-centric view (which is like the one in the link, i.e. we look at one particular feature and see things like which tokens fire strongest on that feature) and a prompt-centric view (where we look at once particular prompt and see which features fire strongest on that prompt according to a variety of different metrics).

Install with pip install sae-vis. Link to PyPI page here.

Important note - this repo was significantly restructured in March 2024 (we'll remove this message at the end of April). The recent changes include:

• The ability to view multiple features on the same page (rather than just one feature at a time)
• D3-backed visualisations (which can do things like add lines to histograms as you hover over tokens)
• More freedom to customize exactly what the visualisation looks like (we provide full cutomizability, rather than just being able to change certain parameters)

Here is a link to a Google Drive folder containing 3 files:

• User Guide, which covers the basics of how to use the repo (the core essentials haven't changed much from the previous version, but there are significantly more features)
• Dev Guide, which we recommend for anyone who wants to understand how the repo works (and make edits to it)
• Demo, which is a Colab notebook that gives a few examples

In the demo Colab, we show the two different types of vis which are supported by this library:

1. Feature-centric vis, where you look at a single feature and see e.g. which sequences in a large dataset this feature fires strongest on.

2. Prompt-centric vis, where you input a custom prompt and see which features score highest on that prompt, according to a variety of possible metrics.

To cite this work, you can use this bibtex citation:

@misc{sae_vis,
    title  = {{SAE Visualizer}},
    author = {Callum McDougall},
    howpublished    = {\url{https://github.com/callummcdougall/sae_vis}},
    year   = {2024}
}

This project is uses Poetry for dependency management. After cloning the repo, install dependencies with poetry install.

This project uses Ruff for formatting and linting, Pyright for type-checking, and Pytest for tests. If you submit a PR, make sure that your code passes all checks. You can run all checks with make check-all.

• 0.2.9 - added table for pairwise feature correlations (not just encoder-B correlations)
• 0.2.10 - fix some anomalous characters
• 0.2.11 - update PyPI with longer description
• 0.2.12 - fix height parameter of config, add videos to PyPI description
• 0.2.13 - add to dependencies, and fix SAELens section
• 0.2.14 - fix mistake in dependencies
• 0.2.15 - refactor to support eventual scatterplot-based feature browser, fix ’ HTML
• 0.2.16 - allow disabling buffer in feature generation, fix demo notebook, fix sae-lens compatibility & type checking
• 0.2.17 - use main branch of sae-lens
• 0.2.18 - remove circular dependency with sae-lens
• 0.2.19 - formatting, error-checking
• 0.2.20 - fix bugs, remove use of batch_size in config
• 0.2.21 - formatting