Would probably be better to demonstrate by exsmple how this approach is used to train on sensor data and then use it (as is hinted by the author) instead of acknowledging that the klingon poc is useless.
I understand that you are alluring that maximum training-state memory, not parameter count, is the key variable here. So you better start with the smallest model, and go to for highest training data quality, with the outlook of coupling systems together?
Very cool project! Sounds like it was a fun challenge :)
I wonder when we'll start seeing clusters of ESP32-S3s... not sure how interconnects would go though, but I guess the interconnect wouldn't be the bottleneck anyway.
Correct. No autograd: the expressions for the gradients are written out explicitly in C. There's a gradient check in tests/ that verifies them against centred finite differences on the published header, worst relative error 1.07e-08.
This small Klingon speaking language model was trained completely on ESP32. The training took 2 days
Number of parameters: 319K
(Disclaimer) The models is tiny and is not a pocket chatbot. It mistakes and is not capable to support conversation, but that's not the goal of the project
The goals of the project is to bring training to edge devices and it worked out
How can one use it? By using solar panels such device could be turned into autonomous meteorological station
I wonder when we'll start seeing clusters of ESP32-S3s... not sure how interconnects would go though, but I guess the interconnect wouldn't be the bottleneck anyway.
I think it is a small thing.
> Backpropagation (gradients derived by hand)
What does by hand mean in this context?
Also how did you write the readme? It's a curious blend of human and AI writing.
Anyways, it looks like gradients derived by hand means they didn't use autograd. They have written out the expression for dL/dW themselves.
Number of parameters: 319K
(Disclaimer) The models is tiny and is not a pocket chatbot. It mistakes and is not capable to support conversation, but that's not the goal of the project
The goals of the project is to bring training to edge devices and it worked out
How can one use it? By using solar panels such device could be turned into autonomous meteorological station