EMLab's KnowLine offers seven open-weight decision models for RAG, agent harnesses, and enterprise workflows, spanning dense models from 0.8B to 31B plus two MoE options. 🤖https://modelscope.ai/collections/EMLab/KnowLine-Decision
🏆 SOTA on private decision tests: KnowLine-31B-Gen1 achieves 71.86 on Jev Decision Index 0.4.1 Full. The score combines private same-skill and new-domain tests, excluding public-suite results.
⚡ Low latency with sparse activation: KnowLine-35B-A3B-Gen1.5 activates about 3B parameters and scores 65.77 Full, with 47.1 ms median latency on the leaderboard's single RTX PRO 6000 setup.
✅ Measured probability calibration: In the 31B model's public-suite evaluation, approximately 0.9–1.0% of decisions made with at least 95% confidence were wrong. Validate thresholds on your own data.
🎯 Decisions throughout the workflow: Filter retrieved documents, check supporting evidence, select tools, route tickets, and score outputs through a Jev-compatible API supporting up to 64 questions per request.
🛠️ Adapt to your business: Continue post-training with ms-swift. The development loop uses research agents to construct and decontaminate data, train, evaluate, and retain or roll back changes based on evidence.
📜 Weights: Apache 2.0. Serving code: MIT.