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Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived
mayafree
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TaeHyun Kim
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Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived liked a Space 4 days ago
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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 → 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
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 → 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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🖼️ POCKET-Image — the POCKET series goes visual: character-perfect text in any language, on-device
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation — and fixes the one thing nearly every image model gets wrong: text.
Type "안녕하세요" into a typical model and you get "안ㅐ기." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly — 한국어 · 中文 · 日本語 · العربية (RTL) · ไทย · Latin and more — onto any scene you describe.
What it is:
• 100% accurate text, any language — where global models produce gibberish
• Any background from a prompt — text is optional (empty → a pure image)
• No GPU, no NPU — runs on plain CPU + RAM via the POCKET-Core engine
• Measured footprint: 8.6 GB (RTX 3050/4060) · 4.5 GB (offloaded, 6 GB cards) · 13.4 GB (MacBook, 16 GB+)
• Windows · macOS · Linux · fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part — the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
🎨 Studio — generate right here, any language:
FINAL-Bench/POCKET-Image-Studio
🧩 Model card:
FINAL-Bench/POCKET-Image-Zimage
📚 The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
A new model in VIDRAFT's POCKET family. POCKET put 35B-class models on phones and no-GPU PCs. POCKET-Image carries the same "big capability, small hardware" idea into image generation — and fixes the one thing nearly every image model gets wrong: text.
Type "안녕하세요" into a typical model and you get "안ㅐ기." Hangul alone composes 11,172 syllable blocks; Arabic connects its letters; Thai stacks marks. Diffusion models draw scripts as shapes, so they smear. POCKET-Image renders every glyph exactly — 한국어 · 中文 · 日本語 · العربية (RTL) · ไทย · Latin and more — onto any scene you describe.
What it is:
• 100% accurate text, any language — where global models produce gibberish
• Any background from a prompt — text is optional (empty → a pure image)
• No GPU, no NPU — runs on plain CPU + RAM via the POCKET-Core engine
• Measured footprint: 8.6 GB (RTX 3050/4060) · 4.5 GB (offloaded, 6 GB cards) · 13.4 GB (MacBook, 16 GB+)
• Windows · macOS · Linux · fully local, no cloud
Built on the open, commercial-friendly Z-Image (Apache-2.0) foundation.
Honest note: the text is the guaranteed-correct part — the surrounding scene is ordinary generation, so a busy foreground can crowd the letters. We say so; clean backgrounds stay razor-sharp.
🎨 Studio — generate right here, any language:
FINAL-Bench/POCKET-Image-Studio
🧩 Model card:
FINAL-Bench/POCKET-Image-Zimage
📚 The POCKET collection:
https://huggingface.co/collections/FINAL-Bench/pocket-models
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🔵 VKUE — No GPU? Runs anyway.
"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.
So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:
• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s
Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.
Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).
Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.
📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae
VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.
"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.
So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:
• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s
Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.
Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).
Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.
📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae
VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.
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VKUE: No GPU? Runs Anyway — a 34.7B Reasoner on a Laptop and on Bare CPU
FINAL-Bench
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Article
Chitos: From Detection to Proof — An Autonomous Security AI That Actually Exploits
FINAL-Bench
• • 19 reacted to SeaWolf-AI's post with 🔥👀 about 1 month ago
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🐯 Chitos — The Security Scanner That Actually Proves It
Most security scanners hand you a suspect list and walk away. That gap between detection and proof is where attackers live — and it's exactly the gap that Chitos was built to close.
Chitos is the successor to Mythos, a static analyzer built for quick code health checks. Mythos was good at pattern matching — spotting dangerous sinks, mapping CWEs, producing readable reports. But static analysis has a structural ceiling. A rule that sees eval(user_input) can tell you that looks dangerous. It cannot tell you whether the input is reachable, whether sanitization three layers up covers this path, or whether there's a live exploit chain for your exact framework version. Chitos was built to answer those questions.
🔍 Phase 1 applies 50 language-agnostic rules across Python, JavaScript, Go, Java, C/C++, Rust, PHP, YAML and more — covering injection sinks, deserialization gadgets, credential leakage, broken crypto, and prototype pollution. Every candidate is re-verified before reaching the report. Findings that can't be substantiated are excluded, not handed to you as noise.
🔬 Phase 2 dispatches an autonomous web-search agent to hunt live CVE databases, exploit advisories, and public PoC repositories. It formulates hypotheses, verifies them, and synthesizes a structured threat narrative. This phase needs a user-supplied Claude API key — Phases 1 and 3 run entirely free.
🎯 Phase 3 is where Chitos diverges from everything else. Against targets you own or are authorized to test, it fires real payloads — XSS, SQLi, path traversal, command injection — mutates on block, captures hard evidence, and connects every proven finding into a kill-chain showing which vulnerabilities to remediate first.
No installation. No account. No code sent to third-party APIs.
Article: https://huggingface.co/blog/FINAL-Bench/chitos
Try it now 👉 https://chitos.vidraft.net
Most security scanners hand you a suspect list and walk away. That gap between detection and proof is where attackers live — and it's exactly the gap that Chitos was built to close.
Chitos is the successor to Mythos, a static analyzer built for quick code health checks. Mythos was good at pattern matching — spotting dangerous sinks, mapping CWEs, producing readable reports. But static analysis has a structural ceiling. A rule that sees eval(user_input) can tell you that looks dangerous. It cannot tell you whether the input is reachable, whether sanitization three layers up covers this path, or whether there's a live exploit chain for your exact framework version. Chitos was built to answer those questions.
🔍 Phase 1 applies 50 language-agnostic rules across Python, JavaScript, Go, Java, C/C++, Rust, PHP, YAML and more — covering injection sinks, deserialization gadgets, credential leakage, broken crypto, and prototype pollution. Every candidate is re-verified before reaching the report. Findings that can't be substantiated are excluded, not handed to you as noise.
🔬 Phase 2 dispatches an autonomous web-search agent to hunt live CVE databases, exploit advisories, and public PoC repositories. It formulates hypotheses, verifies them, and synthesizes a structured threat narrative. This phase needs a user-supplied Claude API key — Phases 1 and 3 run entirely free.
🎯 Phase 3 is where Chitos diverges from everything else. Against targets you own or are authorized to test, it fires real payloads — XSS, SQLi, path traversal, command injection — mutates on block, captures hard evidence, and connects every proven finding into a kill-chain showing which vulnerabilities to remediate first.
No installation. No account. No code sent to third-party APIs.
Article: https://huggingface.co/blog/FINAL-Bench/chitos
Try it now 👉 https://chitos.vidraft.net
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