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Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived
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mayafree/Model-Genome-Korea
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🧬 Architecture lineage of Korea's sovereign-AI foundation models — checked with public data In late July 2026, as Korea released self-developed foundation models competing with DeepSeek and Qwen (e.g. LG K-EXAONE 2.0, 750B), interest grew — including a Zhihu thread with 2.7M+ views (→ https://www.zhihu.com/question/2067512422555029717 ) — over whether these models are trained from scratch or built on foreign open-weights. Sharing a tool that answers this with public data rather than opinion. 🔗 Model Genome Korea → https://huggingface.co/spaces/mayafree/Model-Genome-Korea It classifies the public models of 9 Korean organizations that released "self-developed, from-scratch foundation models" on HuggingFace — 3 large enterprises (LG, NAVER, Kakao), 2 telcos (SKT, KT), 2 mid-size firms (NCSOFT, Upstage), 2 startups (Motif, VIDRAFT) — on two axes measured from public config.json + model weights: • Architecture fingerprint — does model_type + (hidden·intermediate·layers) match a foreign open-weight model • Weight fingerprint — embedding similarity (from-scratch vs continued-pretraining) Genotypes: 🟢 Native · 🔵 Adapted · 🟡 Mixed · 🔴 Ported The results are not uniform. Some models match foreign architectures (Qwen, Llama, …) exactly; others use self-built architectures and weights with no foreign match. Which company/model falls where is shown per model in the Space, along with attention originality, license, and reproducible open-source status. This is a neutral transparency tool, not an accusation — building foundation models on open-weight bases is a legitimate, industry-standard practice. The exact same yardstick is applied to every model, without exception. Features a 3D lineage graph, search, EN / 中文 / 한국어, and dark mode. Corrections are welcome via the Community tab. Articles: https://huggingface.co/blog/mayafree/model-dna #KoreanAI #LLM #ModelLineage #OpenSource #SovereignAI
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