Published: April 30, 2026 NAM impact: NAM’s training code depends on PyTorch Lightning. Users who installed the affected Lightning versions should take action. Overview A supply-chain security incident has affected certain versions of PyTorch Lightning distributed via PyPI. Lightning AI has published an official advisory identifying versions 2.6.2 and 2.6.3 as compromised. These versions should be considered unsafe. Neural Amp Modeler (NAM) uses PyTorch Lightning for training

We're one step closer to A2 today, and I've released an update to NeuralAmpModelerCore, version 0.5.0: https://github.com/sdatkinson/NeuralAmpModelerCore/releases/tag/v0.5.0 Builders interested in how A2 works should aim to support core version 0.5.0 or later. Work isn't entirely compelte yet-- while 0.5.0 will be able to process A2 models correctly , optimized implementations are in development and will be released soon in a follow-up version (0.5.1). If you just want to und

Progress on Architecture A2 has been coming along well, and I'm pretty happy with the results that we've gotten out of Stage 4 of the work (optimizing A2). With that, development will enter the fifth and final stage soon: listening tests . If you haven't already, please sign up to participate by filling out this Google Form . Please also share it with people you know--the more responses we get, the better I can understand how A2 is doing! Thanks for your patience; I'm re

This post is part of a series on Architecture A2 . I've been working on designing NAM's new default architecture, "A2". In this blog post, I wanted to give stakeholders some insight into what I've seen so far in Stage 4 ("Optimize A2", from the original blog post linked above). The work isn't done yet--but I do want to share some information and how it's influencing the way I'm thinking. If you're a builder who's looking to prioritize your work, then this may be useful. Test

Update 2026-02-12: The test models have been updated to "v4" to fix some identified issues. Please re-download the latest if you haven't begun testing. If you've already sent me results, don't worry about it--I'll note the discrepancies and account for them. The core repo has also been updated to v0.4.0.rc3 Progress is coming along on work to develop Architecture A2 , NAM's update to the "standard WaveNet" and its lite/feather/nano variations (retroactively, "A1"). NeuralAmpM

In order to assist with Stage 2 of the Architecture A2's development , I've (finally!) created documentation for the real-time library, and I've integrated v0.4.0 into NeuralAmpModelerPlugin. Core library documentation I've completed a first pass at documentation for the library. You can access it online here: https://neuralampmodelercore.readthedocs.io/en/latest/ Aside from the usual API documentation, I've written a more in-depth description of how the WaveNet actually wor

Update 2026-01-29: v0.4.0.rc2 is released with some updates. I've put up a release candidate for NeuralAmpModelerCore v0.4.0. You can access it here: https://github.com/sdatkinson/NeuralAmpModelerCore/releases/tag/v0.4.0.rc2 This is a release candidate and is hot off the press. If you encounter any bugs, please either create a bug report or get in contact with me. This release marks the completion of Stage 1 of the development of Architecture A2 - If you would like to supp

Blog posts about A2: NeuralAmpModelerCore v0.4.0.rc2 is released (2026-01-21) NeuralAmpModelerCore documentation and demo integrations are live (2026-01-21) Test Models are available (2026-01-29) Sign up for listening tests (2026-04-13) In quarter 1 of 2026, I plan to develop and release a new default architecture for snapshot NAM models. I'm going to name this architecture " A2 ". Here's the plan. What's going to happen Nothing is going away. The existing "standard Wav

I'm excited to share a paper I've recently completed: "Slimmable NAM: Slimmable NAM: Neural Amp Models with adjustable runtime computational cost" The paper was accepted for the 2025 NeurIPS workshop on Artificial Intelligence for Music: Where Creativity Meets Computation ; I'll be presenting it in 2 weeks in San Diego. In the meantime, anyone can access it here: https://arxiv.org/abs/2511.07470 . A video demonstrating the method as well as open-source code and a demo plugin

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