[Appreciation Post] Gemma 4 E2B. My New Daily Driver 😁
idk but this thing feels like magic in the palm of my hands. I am running it on my Pixel 10 Pro with AI Edge Gallery by Google. The phone itself is only using CPU acceleration for some reason and therefore the E4B version felt a little to slow. However, with the E2B it runs perfect. Faster than I can read and follow along and has some function calling in the app. I am running it at the max 32K context and switch thinking on and off when I need. It seem ridiculously intelligent. Feels like a 7b model. I'm sure there is some recency bias here. But just having it run at the speed it does on my phone with it's intelligence feels special. Are you guys having a good experience with the E models? submitted by /u/Prestigious-Use5483 [link] [comments]
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Mean field sequence: an introduction
This is the first post in a planned series about mean field theory by Dmitry and Lauren (this post was generated by Dmitry with lots of input from Lauren, and was split into two parts, the second of which is written jointly). These posts are a combination of an explainer and some original research/ experiments. The goal of these posts is to explain an approach to understanding and interpreting model internals which we informally denote "mean field theory" or MFT. In the literature, the closest matching term is "adaptive mean field theory". We will use the term loosely to denote a rich emerging literature that applies many-body thermodynamic methods to neural net interpretability. It includes work on both Bayesian learning and dynamics (SGD), and work in wider "NNFT" (neural net field theor
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