TL;DR. A public method called Model DNA — with a live tool — lets outsiders estimate whether an LLM was trained from scratch or derived from an open-weight base, using nothing but public artifacts: config.json, tokenizer.json, and embedding weights. This is a technical deep-dive that cites and reproduces that method in PyTorch, then analyzes where it holds up and where it breaks.
Key takeaways
Three public signals — architecture config, tokenizer overlap, and embedding-space similarity via Linear CKA — combine to place a model on a lineage spectrum.
One matching field is noise; five at once is a fingerprint. Provenance is a preponderance-of-evidence judgment, not a single test.
The method's real strengths are reproducibility and rotation-invariant similarity; its real limits are the continued-pretraining gray zone, threshold sensitivity, and an embedding-only view.
