In this talk, Chris Wendler presents his recent work on using sparse autoencoders for diffusion models. In this work, they train SAEs on SDXL Turbo, finding ...

NDIF is a research computing project that enables researchers and students to crack open the mysteries inside large-scale AI systems.

Do you have a research project where you plan to study many different models? NDIF will soon be deploying model hot-swapping, which will enable users to access any HuggingFace model remotely via NDIF. We are soliciting applications for a pilot program to beta test our hot-swapping functionality on real research. By participating, you will: Be in the first cohort of users to access models beyond our whitelist (including checkpoints) Directly control which models are hosted on the NDIF backend Receive 1:1 research and technical support from NDIF team Give feedback to NDIF, guiding future user experience Application Information: Apply by October 1st, 2025 Acceptance by October 15th, 2025 Applications will be reviewed based on impact, feasibility, and fit with the NDIF/NNsight platform We are particularly interested in supporting work on model checkpoints and training dynamics Please email [email protected] with any questions.

09:30 AM - 09:40 AM: Opening Remarks (David Bau) 09:40 AM - 10:00 AM: Keynote 1: Lee Sharkey: "Mech Interp: Where should we go from here?" 10:00 AM - 10:10 A...

About:The New England Mechanistic Interpretability (NEMI) workshop aims to bring together academic and industry researchers from the New England and surround...

We're a research computing project cracking open the mysteries inside large-scale AI systems. The NSF National Deep Inference Fabric consists of a unique combination of hardware and software that provides a remotely-accessible computing resource for scientists and students to perform detailed and reproducible experiments on large pretrained AI models, such as open large language models. We aim to make AI interpretability research more accessible through this channel by publishing lectures and educational content covering real interpretability research.

We're a research computing project cracking open the mysteries inside large-scale AI systems. The NSF National Deep Inference Fabric consists of a unique combination of hardware and software that provides a remotely-accessible computing resource for scientists and students to perform detailed and reproducible experiments on large pretrained AI models, such as open large language models. We aim to make AI interpretability research more accessible through this channel by publishing lectures and educational content covering real interpretability research.

David Bau is an Assistant Professor of Computer Science at Northeastern University's Khoury College. His lab studies the structure and interpretation of deep...

We welcome suggestions for tutorials using the NNsight library to explore model internals! If you would like to share an idea for an NNsight mini paper tutorial, please fill out the form below. Note: if you wish to share an existing walkthrough notebook on the NNsight website, please go fill out this form.

NNsight 0.5 Prerelease Now Available We’re excited to announce the prerelease of NNsight 0.5, packed with new features that you’ve been requesting. What’s New Enhanced intermediate value access during forward passes Flexible code execution within NNsight’s tracing context Improved debugging capabilities for smoother development Get Started Ready to explore NNsight 0.5? Our comprehensive Colab notebook includes installation instructions and feature overviews: Try NNsight 0.5 → Share Your Feed...

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