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peremartra/Rearchitecting-LLMs

Official code for the Manning book on structural LLM optimization: depth/width pruning, knowledge distillation, and attention optimization, runnable on free Colab GPUs.

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Paper → model → repo connections mined from source citations (Tier-1 exact match).

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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.

PossiblePossibly related (embedding) · 48%Evaluate a model properly →
PossiblePossibly related (embedding) · 48%I shrank a transformer until every number fitted on the screen and made the weights editable [R] →
PossiblePossibly related (embedding) · 47%The gap between open weights LLMs and closed source LLMs →
PossiblePossibly related (embedding) · 46%How're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D] →
PossiblePossibly related (embedding) · 49%H64LM: A 249M-parameter Mixture-of-Experts Transformer built from scratch in PyTorch [P] →

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tutorialEvaluate a model properly

Covers

newsI shrank a transformer until every number fitted on the screen and made the weights editable [R]newsThe gap between open weights LLMs and closed source LLMs newsHow're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D]

Covers (incoming)

newsH64LM: A 249M-parameter Mixture-of-Experts Transformer built from scratch in PyTorch [P]

Related across the graph

newsThe gap between open weights LLMs and closed source LLMs newsHow're you deploying LLMs in production now-a-days? What's the best and most affordable way? [D]newsI shrank a transformer until every number fitted on the screen and made the weights editable [R]newsH64LM: A 249M-parameter Mixture-of-Experts Transformer built from scratch in PyTorch [P]tutorialEvaluate a model properly

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