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liked a model 42 minutes ago
FINAL-Bench/Darwin-27B-ZTC-v2
reacted to SeaWolf-AI's post with 🚀 about 2 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 2 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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