ecco

Hello HN, author here. Language models are absolutely fascinating tools. I believe it would pay for software engineers to have a sense of their capabilities and how they function. The article showcases a few views to expose the inner workings of the model, but also simple UI for interacting with a language model to get a sense for how they work and generate words. If you prefer video, I have also recently released a video [1] with PyData to provide an intro to language models and their applications and how we're trying to make Transformer-based ones more transparent with Ecco[2]. Contributors are welcome! [1] https://www.youtube.com/watch?v=rHrItfNeuh0 [2] https://www.eccox.io/ and https://github.com/jalammar/ecco Thanks mods for merging submissions. Happy to get feedback , thoughts, or questions.

I actually started with PCA. But NMF proved more understandable since negative dimensions in PCA are hard to interpret. I didn't consider UMAP, but would be interested to see how it performs here. It should be easy, yeah. for NMF, the activations vector is reshaped from (layers, neurons, token position) down into (layers/neurons, token position). And we present that to sklearn's NMF model. I would assume UMAP would operate on that same matrix. That matrix is called 'merged_act' and is located here: https://github.com/jalammar/ecco/blob/1e957a4c1c9bd49c203993...