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Jinki Jeong

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liked a model about 9 hours ago
FINAL-Bench/Darwin-35B-A3B-Opus-Q8-GGUF
reacted to SeaWolf-AI's post with šŸ¤— 1 day ago
šŸ§¬ Darwin-35B-A3B-Opus ā€” The Child That Surpassed Both Parents What if a merged model could beat both its parents? We proved it can. Darwin-35B-A3B-Opus is a 35B MoE model (3B active) built with our Darwin V5 engine ā€” the first evolution system that CT-scans parent models before merging them. šŸ¤— Model: https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus The result speaks for itself: GPQA Diamond 90.0%, versus Father (Qwen3.5-35B-A3B) at 84.2% and Mother (Claude 4.6 Opus Distilled) at 85.0%. That's +6.9% over Father and +5.9% over Mother. Not a tradeoff ā€” a genuine leap. Meanwhile, MMMLU sits at 85.0% (Father: 85.2%), multimodal is fully intact, and all 201 languages are preserved. How? Model MRI changed everything. Traditional merging is guesswork. Darwin V4 added evolution. Darwin V5 added X-ray vision. Model MRI scans each parent layer by layer and discovers: Mother's L34ā€“L38 is the reasoning engine (peak cosine distance), 50ā€“65% of Mother's experts are dead (killed by text-only distillation), and Father is a healthy generalist with every expert alive. The prescription: transplant Mother's reasoning brain at L38 (90% weight), replace her dead experts with Father's living ones, and let Father's router handle the output layer. Reasoning went up. Versatility stayed intact. No tradeoff ā€” just evolution. 35B total, 3B active (MoE) Ā· GPQA Diamond 90.0% Ā· MMMLU 85.0% (201 languages) Ā· Multimodal Image & Video Ā· 262K native context Ā· 147.8 tok/s on H100 Ā· Runs on a single RTX 4090 (Q4) Ā· Apache 2.0 Darwin V5's full algorithm and technical details will be released alongside an upcoming paper. šŸš€ Live Demo: https://huggingface.co/spaces/FINAL-Bench/Darwin-35B-A3B-Opus šŸ† FINAL Bench Leaderboard: https://huggingface.co/spaces/FINAL-Bench/Leaderboard šŸ“Š ALL Bench Leaderboard: https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard Built by VIDRAFT Ā· Supported by the Korean Government GPU Support Program
reacted to SeaWolf-AI's post with šŸ‘ 1 day ago
šŸ§¬ Darwin-35B-A3B-Opus ā€” The Child That Surpassed Both Parents What if a merged model could beat both its parents? We proved it can. Darwin-35B-A3B-Opus is a 35B MoE model (3B active) built with our Darwin V5 engine ā€” the first evolution system that CT-scans parent models before merging them. šŸ¤— Model: https://huggingface.co/FINAL-Bench/Darwin-35B-A3B-Opus The result speaks for itself: GPQA Diamond 90.0%, versus Father (Qwen3.5-35B-A3B) at 84.2% and Mother (Claude 4.6 Opus Distilled) at 85.0%. That's +6.9% over Father and +5.9% over Mother. Not a tradeoff ā€” a genuine leap. Meanwhile, MMMLU sits at 85.0% (Father: 85.2%), multimodal is fully intact, and all 201 languages are preserved. How? Model MRI changed everything. Traditional merging is guesswork. Darwin V4 added evolution. Darwin V5 added X-ray vision. Model MRI scans each parent layer by layer and discovers: Mother's L34ā€“L38 is the reasoning engine (peak cosine distance), 50ā€“65% of Mother's experts are dead (killed by text-only distillation), and Father is a healthy generalist with every expert alive. The prescription: transplant Mother's reasoning brain at L38 (90% weight), replace her dead experts with Father's living ones, and let Father's router handle the output layer. Reasoning went up. Versatility stayed intact. No tradeoff ā€” just evolution. 35B total, 3B active (MoE) Ā· GPQA Diamond 90.0% Ā· MMMLU 85.0% (201 languages) Ā· Multimodal Image & Video Ā· 262K native context Ā· 147.8 tok/s on H100 Ā· Runs on a single RTX 4090 (Q4) Ā· Apache 2.0 Darwin V5's full algorithm and technical details will be released alongside an upcoming paper. šŸš€ Live Demo: https://huggingface.co/spaces/FINAL-Bench/Darwin-35B-A3B-Opus šŸ† FINAL Bench Leaderboard: https://huggingface.co/spaces/FINAL-Bench/Leaderboard šŸ“Š ALL Bench Leaderboard: https://huggingface.co/spaces/FINAL-Bench/all-bench-leaderboard Built by VIDRAFT Ā· Supported by the Korean Government GPU Support Program
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