Show HN: TensorViz – Explore PyTorch models inside VS Code

TensorViz launched as a VS Code tool that lets developers explore 29 interactive PyTorch architectures, including ResidualClassifier, without installing Python or any additional software. The tool offers guided tours of model internals spanning attention, convolution, residual paths, GANs, diffusion, and Evoformer designs, and each model ships with its source and a small reproduction recipe so users can open their own PyTorch classes in the editor.

START HEREExplore graph ResidualClassifier4-step tour TensorViz https://tensorviz.dev/explore/residual-classifier/ The residual path What happens where the skip path rejoins? MODEL EXPLORER / CURATED BY TENSORVIZ 29 interactive PyTorch architectures. Follow a guided tour, inspect captured shapes, and take the source back to your own project. No installation · No Python needed CHOOSE A LEARNING PATH Start with attention, change one design choice at a time, then connect the pieces in LLaMA. Follow convolution, residual paths, U-shaped skips and image patches through landmark vision designs. Explore alignment, bidirectional pretraining, image–text pairing, low-rank adaptation and reasoning post-training. Compare the GAN game with noise prediction, latent-space denoising and a Transformer diffusion backbone. Inspect selective recurrent state, Evoformer sequence/pair coupling and promptable mask decoding. A teaching order, not a claim that each later model adopted every earlier idea. 29 models to explore Where does an image become a classifier input? How do queries, keys and values meet? Component · 2019 What does normalization change in one token vector? Component · 2020 Why does this feed-forward block split into two branches? Architecture · 2017 How does a decoder form a next-token distribution? What happens to the shortcut when normalization moves? Component · 2023 Which attention heads can share a key/value cache? Component · 2021 Where does token position enter the attention score? Inference method · 2022 Can exact attention avoid storing the entire score matrix? Component · 2024 What must MLA store to avoid reconstructing every key and value? Component · 2017 How can a token use only part of a larger network? Training method · 2021 How can two small matrices adapt a frozen linear layer? Architecture · 2023 Where do RMSNorm, RoPE and SwiGLU meet in an actual decoder block? Architecture · 2012 How do five convolutions turn an image into a classification vector? Architecture · 2015 What can flow through a shortcut when a residual branch changes? How does a segmentation decoder recover features lost during downsampling? Architecture · 2020 Where does an image become a sequence for a Transformer? Component · 2014 How does a recurrent decoder decide which source positions to consult? Architecture · 2018 What changes when the encoder can see both left and right context? Training method · 2014 Which network is being updated, and what is it trying to change? Training method · 2020 How does a model learn to reverse corruption? Architecture · 2021 Why denoise a latent, and where does text enter? Architecture · 2022 What changes when a Transformer denoises latent patches? Where do the image and text meet? How do image features and a user prompt become a mask? How does the input decide what a recurrent state retains? How do sequence and pair representations interact before structure is produced? Training method · 2025 What changes in the learning process while the base architecture stays the same? Try another idea, or return to the full collection. BRING IT BACK TO YOUR PROJECT Every model includes its source and a small reproduction recipe. Open your own PyTorch class with tensorViz in VS Code.