Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived

Model Genome proposes a reproducible method for assessing whether publicly released large language models were trained from scratch or derived from existing open-weight models, using publicly available configuration files, tokenizers, and embeddings. Its primary evidence combines architecture fingerprints from config.json, where exact matches across several model dimensions can indicate an adopted reference design, and tokenizer vocabulary overlap, which can reveal reused, reduced, or independently trained vocabularies. The project finds that direct row-by-row embedding cosine similarity is ineffective because neural representations can be arbitrarily rotated, while rotation-invariant linear CKA can support evidence of independent pretraining but cannot reliably distinguish continued pretraining from derivation. It also tracks attention-mechanism diversity as a rough indicator of architectural novelty and presents models through genotype categories ranging from Native to Ported. Applied consistently to public models from nine Korean organizations, the analysis reports a mixed landscape of independently developed, partially inherited, and closely matched models, while emphasizing that use of open-weight bases is legitimate and that the tool identifies technical lineage rather than misconduct.

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