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reacted to SeaWolf-AI's post with 👍 about 7 hours ago
🏆 Darwin-27B-ZTC-v2 just took #1 on the System One Mosaic Benchmark (S1MB). S1MB compares 102 models across 137 specialized benchmarks, in three task types: Noul (assess a condition), Choice (select an option), Score (rate on a scale). Ranking is by overall Borda score. 📊 Top of the board 🥇 Darwin ZTC v2 (FINAL-Bench) 89.58 🥈 OpenJev-27B 87.50 🥉 AutoJev-27B 87.07 4️⃣ Eikos 27B 85.43 5️⃣ Jev 1.13 85.05 🔎 Ranks 2 to 5 are all the JEV family (TypeSafe AI's System One model, from ex-OpenAI researchers). S1MB exists to compare these System One judges, so leading it is the headline. ⚙️ Why a zero-token judge wins here 🔹 It does not generate. It reads the input and typed questions and returns a calibrated distribution in a single forward pass. 🔹 Zero generated tokens, no decoding loop, so latency and cost stay low. 🔹 Holds up out of distribution too: General Noul 96.00, General Choice 99.34. It is also #1 on the typed-decisions leaderboard (0.743, zero-shot). Same message from both: a deterministic, calibrated judge at one forward pass per call. 🔗 Model: https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v2 🔗 Leaderboard: https://huggingface.co/spaces/hotchpotch/S1MB-leaderboard Standings move as new models are added. Numbers reflect the board at the time of writing. 🙌
reacted to SeaWolf-AI's post with 🔥 about 7 hours ago
🧠 We just released Darwin-27B-ZTC, a judgment engine that reaches a verdict without generating anything. Most LLMs answer by generating, decoding one token at a time. Darwin-27B-ZTC takes a different route. ⚙️ How it works 🔹 It makes its call in a single forward pass. 🔹 Zero generated tokens, and no decoding loop. 🔹 That keeps latency and cost far below what a generative model needs. 🎯 What it judges 🔹 It handles several question types: free-form correctness (noul), multiple choice (choice), and scoring (score). 🔹 For each one it hands back a calibrated confidence, not just an answer. 📊 How well calibrated (measured) 🔹 KL 0.204, Brier 0.097, so the confidence it reports lines up with what actually happens. 🔹 0.743 accuracy (zero-shot, general split), across 2,000 judgments with zero errors. 🔹 By type: noul 0.847, choice 0.723, score 0.675. 🔹 None of the benchmark's train split went into it. It is pure zero-shot. 🚀 Where it fits 🔹 Grading at scale, model routing, safety gating, anywhere you want a fast decision without paying for generation. 🏆 It currently sits at #1 on the official typed-decisions leaderboard on Hugging Face (0.743 accuracy, zero-shot). 🔗 Links Model: https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC Leaderboard: https://huggingface.co/datasets/LocalLLaMA/typed-decisions Curious to hear what you make of the single-pass, no-generation approach. 🙌
reacted to SeaWolf-AI's post with 👍 about 7 hours ago
🧠 We just released Darwin-27B-ZTC, a judgment engine that reaches a verdict without generating anything. Most LLMs answer by generating, decoding one token at a time. Darwin-27B-ZTC takes a different route. ⚙️ How it works 🔹 It makes its call in a single forward pass. 🔹 Zero generated tokens, and no decoding loop. 🔹 That keeps latency and cost far below what a generative model needs. 🎯 What it judges 🔹 It handles several question types: free-form correctness (noul), multiple choice (choice), and scoring (score). 🔹 For each one it hands back a calibrated confidence, not just an answer. 📊 How well calibrated (measured) 🔹 KL 0.204, Brier 0.097, so the confidence it reports lines up with what actually happens. 🔹 0.743 accuracy (zero-shot, general split), across 2,000 judgments with zero errors. 🔹 By type: noul 0.847, choice 0.723, score 0.675. 🔹 None of the benchmark's train split went into it. It is pure zero-shot. 🚀 Where it fits 🔹 Grading at scale, model routing, safety gating, anywhere you want a fast decision without paying for generation. 🏆 It currently sits at #1 on the official typed-decisions leaderboard on Hugging Face (0.743 accuracy, zero-shot). 🔗 Links Model: https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC Leaderboard: https://huggingface.co/datasets/LocalLLaMA/typed-decisions Curious to hear what you make of the single-pass, no-generation approach. 🙌
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