cognita VS minGPT

Hi, I've come across an open-source API-driven RAG framework launched recently. It's different from other frameworks in a lot of context. Give it a try and let me know your thoughts: https://github.com/truefoundry/cognita

minGPT (Karpathy): https://github.com/karpathy/minGPT

Next, some foundational textbooks for general ML and deep learning:

Language Model novice here. I was going through the README section of minGPT and read this line.

The first network displayed with working weights is a tiny such network, which sorts a small list of the letters A, B, and C. This is the demo example model from Andrej Karpathy's minGPT implementation.

Tried it once. Its promise is to take the dataset's seasonal trend into account, which makes sense for Facebook's original use case.

We ran it on such a dataset and found out that directly using https://github.com/karpathy/minGPT consistently gives a better result. So we ended up using the output of Prophet as an input feature to a neural network, but the result was not improved in any significant way.

Sure, im trying to regenerate a bunch of complex numbers based on their absolute value. So im trying to embed these absolute values and then using gpt model(probably mini gpt) try to recover the original comples numbers. There is a certain connection between these complex numbers and their order which im not capable of explaining yet. Im hoping the model would be capable of recognizing certain sequences of these absolute values and match them with the desired complex counterparts (by training the model).

minGPT (https://github.com/karpathy/minGPT)

Check out minGPT and nanoGPT from Karpathy, he puts out some of the best machine learning tutorials and teaching content.