newsReddit r/MachineLearningTrust 72 · CommunityPublished 1mo agoLive · 1mo ago
I shrank a transformer until every number fitted on the screen and made the weights editable [R]
I've been teaching myself how LLMs actually work, not at the API level, but down to the matrix multiplications. To force myself to really understand the forward pass, I first built a complete transformer by hand in a spreadsheet from embeddings through to the loss. Then I turned the forward pass into a web page so it's easier to share. It's a full transformer (single attention head, single block) shrunk to the smallest size where every single number still
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- LinkedLinked via unknownengineering87/llm-atlas →
- LinkedLinked via unknownBuild your first transformer from scratch →
- LinkedLinked via unknownPost-Training Pruning for Diffusion Transformers →
- PossiblePossibly related (embedding) · 48%teilomillet/retrain →
- PossiblePossibly related (embedding) · 48%peremartra/Rearchitecting-LLMs →
- PossiblePossibly related (embedding) · 45%T^2MLR: Transformer with Temporal Middle-Layer Recurrence →
- PossiblePossibly related (embedding) · 53%Reduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM Inference →
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paperT^2MLR: Transformer with Temporal Middle-Layer Recurrencerepoteilomillet/retrainrepoengineering87/llm-atlaspaperReduced Matrix Multiplication: Input-Adaptive Matrix-Product Reduction for LLM InferencetutorialBuild your first transformer from scratchrepoperemartra/Rearchitecting-LLMspaperPost-Training Pruning for Diffusion Transformers
