Research artifacts for the paper "Wiring Beats Blending: Structure-Aware Compensation for Transformer Downscaling" (arXiv:2608.02829, v3; v1–v2 carried the title "What Transfers Between Transformer Sizes — and What Doesn't"). The paper asks whether a pretrained large model can be converted into a smaller sibling instead of training the small size from scratch. These checkpoints are the endpoints of matched-budget recovery races: every arm gets the same target shape, data order, schedule, and token budget, and only the starting weights differ. The initializations compared: All files are plain PyTorch statedicts for GPTNeoXForCausalLM with the target size's stock Pythia config. They are…
Independent publisher
Ravi Satya Durga Prasad Yenugula
YenugulaAIML
Models in Library1
Datasets in Library0
Models on Hugging Face2
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Published tasks: Text generation.
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