# SAE Lens [](https://pypi.org/project/sae-lens/) [](https://opensource.org/licenses/MIT) [](https://github.com/decoderesearch/SAELens/actions/workflows/build.yml) [](https://github.com/decoderesearch/SAELens/actions/workflows/deploy_docs.yml) [](https://codecov.io/gh/decoderesearch/SAELens) SAELens exists to help researchers: - Train sparse autoencoders. - Analyse sparse autoencoders / research mechanistic interpretability. - Generate insights which make it easier to create safe and aligned AI systems. SAELens inference works with any PyTorch-based model, not just TransformerLens. While we provide deep integration with TransformerLens via `HookedSAETransformer`, SAEs can be used with Hugging Face Transformers, NNsight, or any other framework by extracting activations and passing them to the SAE's `encode()` and `decode()` methods. Please refer to the [documentation](https://decoderesearch.github.io/SAELens/) for information on how to: - Download and Analyse pre-trained sparse autoencoders. - Train your own sparse autoencoders. - Generate feature dashboards with the [SAE-Vis Library](https://github.com/callummcdougall/sae_vis/tree/main). SAE Lens is the result of many contributors working collectively to improve humanity's understanding of neural networks, many of whom are motivated by a desire to [safeguard humanity from risks posed by artificial intelligence](https://80000hours.org/problem-profiles/artificial-intelligence/). This library is maintained by [Joseph Bloom](https://www.decoderesearch.com/), [Curt Tigges](https://curttigges.com/), [Anthony Duong](https://github.com/anthonyduong9) and [David Chanin](https://github.com/chanind). ## Loading Pre-trained SAEs. Pre-trained SAEs for various models can be imported via SAE Lens. See this [page](https://decoderesearch.github.io/SAELens/latest/pretrained_saes/) for a list of all SAEs. ## Migrating to SAELens v6 The new v6 update is a major refactor to SAELens and changes the way training code is structured. Check out the [migration guide](https://decoderesearch.github.io/SAELens/latest/migrating/) for more details. ## Tutorials - [SAE Lens + Neuronpedia](tutorials/tutorial_2_0.ipynb)[](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/tutorial_2_0.ipynb) - [Loading and Analysing Pre-Trained Sparse Autoencoders](tutorials/basic_loading_and_analysing.ipynb) [](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb) - [Understanding SAE Features with the Logit Lens](tutorials/logits_lens_with_features.ipynb) [](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/logits_lens_with_features.ipynb) - [Training a Sparse Autoencoder](tutorials/training_a_sparse_autoencoder.ipynb) [](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb) - [Training SAEs on Synthetic Data](tutorials/training_saes_on_synthetic_data.ipynb) [](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/training_saes_on_synthetic_data.ipynb) - [SynthSAEBench: Evaluating SAE Architectures on Synthetic Data](tutorials/synth_sae_bench.ipynb) [](https://githubtocolab.com/decoderesearch/SAELens/blob/main/tutorials/synth_sae_bench.ipynb) ## Join the Slack! Feel free to join the [Open Source Mechanistic Interpretability Slack](https://join.slack.com/t/opensourcemechanistic/shared_invite/zt-3z9o0hxjl-MDX9pbATO2qESOazNDLpdQ) for support! ## Other SAE Projects - [dictionary-learning](https://github.com/saprmarks/dictionary_learning): An SAE training library that focuses on having hackable code. - [Sparsify](https://github.com/EleutherAI/sparsify): A lean SAE training library focused on TopK SAEs. - [Overcomplete](https://github.com/KempnerInstitute/overcomplete): SAE training library focused on vision models. - [SAE-Vis](https://github.com/callummcdougall/sae_vis): A library for visualizing SAE features, works with SAELens. - [SAEBench](https://github.com/adamkarvonen/SAEBench): A suite of LLM SAE benchmarks, works with SAELens. ## Citation Please cite the package as follows: ``` @misc{bloom2024saetrainingcodebase, title = {SAELens}, author = {Bloom, Joseph and Tigges, Curt and Duong, Anthony and Chanin, David}, year = {2024}, howpublished = {\url{https://github.com/decoderesearch/SAELens}}, } ```