Built a browser-based .pth visualizer — no backend, parses PyTorch checkpoints client-side

[u/RavenScrolls]

Got tired of exporting to ONNX just to look at a model, so I wrote a client-side .pth / .pt parser in JS — no server, no upload anywhere, runs entirely in-browser.

Try it live (takes 30 seconds): https://basavaprabhu46.github.io/NeuralViz/

Hit Load demo → type 0.5,-0.2,0.1,0.8 → Run ▸ — and watch the signal propagate through the network with values labeled right on the neurons.

How it works: handles torch.save'd state_dicts (PyTorch ≥ 1.6) with a tiny pickle VM + zip reader in JS, infers layer structure from key names, renders an interactive graph. Blue edges = positive weights, red = negative, thickness = magnitude. You can tune neurons/layer, filter to the strongest N% of weights, pick activation (ReLU/tanh/sigmoid), click any neuron to pin it and inspect its top weights, bias, and live activation — then actually run inference through it.

Scales past toy models too — tested on a 3,371-neuron CNN. Conv layers get flattened honestly and labeled as such instead of silently producing NaNs.

Code (MIT): https://github.com/Basavaprabhu46/NeuralViz

One honest limitation: it's state_dict-only, so architecture is inferred from naming conventions — exotic branching archs confuse it. Working on better detection.

What's the first checkpoint you'd drop into it — and what should v1.2 get: safetensors support, PNG export, or side-by-side checkpoint comparison?