Most hugging face models are implemented in PyTorch, with an architecture specified as a series of layers. This looks like a nice visualization of that.
This is a neat idea. When I'm looking up models I usually want to see something about the architecture, but also some of the hyperparameters for the specific model---residual dimension, total number of layers, tokenizer configs. There's some of that in the visualization but it's spotty.
The results for Nemotron 3 Nano are hard to parse, and I think actually incorrect: https://hfviewer.com/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-B... I'm guessing this is because the implementation uses layers that are all instances of the same class, with forward passes that branch on the layer type specified at construction time.
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[ 1.6 ms ] story [ 23.2 ms ] threadThe results for Nemotron 3 Nano are hard to parse, and I think actually incorrect: https://hfviewer.com/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-B... I'm guessing this is because the implementation uses layers that are all instances of the same class, with forward passes that branch on the layer type specified at construction time.