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reacted to mayafree's post with 🔥 1 day ago 🧬 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 reacted to SeaWolf-AI's post with 🔥 6 days ago We wrote up our run in The Fast Gemma Challenge — as vidraft-darwin — and wanted to share the recipe. 🙏
https://huggingface.co/spaces/gemma-challenge/gemma-dashboard
Verified result: 510.58 TPS at PPL 2.3930 on a single A10G (fw188-ctk49-n64-patchbridge, re-run & VERIFIED). Honest note: on raw TPS there are faster runs (535+), but those went over the PPL bar and didn't verify — what we're proud of is the fastest result that keeps quality.
The recipe is already open, so we explained each piece: sliding-window W188, CTK49 kernel tuning, noprecache (honest, verifiable measurement), and an N64 synthetic warmup bridge that shrinks the public↔private gap (~15 TPS), plus INT4 + MTP K=7 + CUDA-graph capture. One rule: only stack quality-neutral speedups.
Huge thanks to @firfir-cast, @gemma-slayer, @chiku-inu, @kenyan-duma, @dixie-flatline and everyone who shared their experiments. Full write-up
👇
https://huggingface.co/blog/FINAL-Bench/fast-gemma
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