Transformer visualizations:

  • https://bbycroft.net/llm

  • https://poloclub.github.io/transformer-explainer/

  • Autograd / miniGPT by hand (blog roadmap)
  • CS336 @ Stanford
  • Transformers from scratch - sensibly written, broken down by topic
  • Provable optimal transport with transformers - optimal transport - math for finding efficiently probability distribution 1 -> prob distrib 2
    • Wasserstein distance - between two probability distribs
    • Sinkhorn algorithm - iterative, converges towards a shortest
  • Transformers learn in-context from gradient descent (ETH Zurich)
    • in-context learning - e.g. english followed by french, a “circuit” appears in the “neurons” called an “induction head”
    • naively this might be, in the output, copying the english words - so the circuit is doing the copy, and then it passes the same tokenization of the english through the french neurons
    • and this, but scaled up to another level of abstraction, is what is generally referred to as in-context learning (src)
    • assertion of the ETH Zurich paper is that gradient descent happens in the forward pass
  • Unelicitable backdoors in Language Models via Cryptographic Transformer Circuits - feb2025, wrote a language to compile sha256 into a pytorch transformer, that only triggers when a particular input is present
  • [Trail of Bits’ Anamorpher] - Hiding text that only appears when an image is downscaled - text then interpreted by the LLM