@Ellieorange8: During a large model algorithm interview, the interviewer smiled and handed me a pen: Write down the Attention mechanism on the whiteboard. I nodded pretending to be confident, took the pen—then my mind went blank. Normally, I just call `F.softmax` in PyTorch and that's it, but when I have to hand-code the underlying operators, I remember the formula but forget all the details. Don't go into the exam unprepared anymore...
Summary
TorchCode is an open-source question bank that turns handwritten deep learning operators (such as Attention, Transformer) into LeetCode-style training. It is based on Jupyter and supports auto-grading, hints, and reference implementations, helping you prepare for large model algorithm interviews.
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During a large model algorithm interview, the interviewer smiles and hands you a pen: “Write the Attention mechanism on the whiteboard.” I nod with feigned confidence, take the pen — and my mind goes blank. Normally I just call F.softmax in PyTorch, but when it comes to handwriting the underlying operator from scratch, I remember the formula but lose all the details. Don’t go into an interview unprepared. I found an open-source question bank: TorchCode — turning “writing deep learning operators by hand” into a LeetCode-style training platform. It’s built on Jupyter with 40 curated problems: from basic operators → Attention → full Transformer → training optimization. Automatic evaluation: real-time checks on output, gradient flow, and numerical stability. Stuck? Get a hint. Done? Check the reference implementation. You can start with one click via Docker / Hugging Face online / Google Colab. GitHub: http://github.com/duoan/TorchCode. If you want to thoroughly grasp the underlying principles of large models, work through these problems and your interviews will feel like a walk in the park. Bookmark it now — you might use it someday.
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