Llama 3.2 Interpretability with Sparse Autoencoders
I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
Llama 3.2 Interpretability with Sparse Autoencoders launched on November 21, 2024 with 579 votes, #35 of 4,990 launches that month and more than 99% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
This project replicates mechanistic interpretability research on large language models using sparse autoencoders, building on published work from Anthropic, OpenAI, and DeepMind. It applies techniques for understanding how neural networks process information to the open-source Llama 3.2 model. The project is aimed at researchers and developers interested in AI interpretability, offering an open-source alternative to proprietary LLM analysis work.
written from the facts on this page · September 2026
From the sources
A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible. - PaulPauls/llama3_interpretability_sae
In the maker’s words, at launch
I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since…
Does a similar job
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