Thank you, @HuggingModels. AutoTrust-JEV-27B-v1.1 has now passed 16K downloads on Hugging Face. It is an open 27B decision model for the high-frequency calls inside agent systems: route a request, select a tool, detect an out-of-scope intent, verify a claim, or choose the next action from a fixed set of options. Instead of producing another paragraph, it returns a probability for every available option — making decisions measurable, routable, and easier to audit. Apache-2.0. Run it locally with vLLM: 📷huggingface.co/autotrust/Aut…

4/ Its highest-scoring area in the reported run is Tools & Automation: 85.19 skill points. The same model-card table reports: • Language Understanding: 73.38 • Retrieval & Classification: 66.97 • Knowledge & Reasoning: 55.16 • Arts & Human Taste: 51.08 A skill score adjusts for task chance level; 0 is chance and 100 is perfect. The public index is not a simple average of the rounded area scores.

5/ Selected task-adaptation results from the card: • 97.06 — CLINC150+OOS intent classification • 96.25 — BFCL tool selection • 93.63 — BANKING77 intent classification • 89.77 — home appliance simulator decisions • 87.36 — API-Bank API-use decisions • 78.24 — HoVer claim verification These are evaluated task scores, not a claim of perfect real-world reliability or end-to-end autonomous-agent readiness.

1/ Introducing AutoTrust-JEV-27B v1.1. A 27B text decision model that assigns probabilities to the answer options supplied with each question. This is for the thousands of bounded calls inside agent workflows: tool choice, request routing, intent classification, API-use decisions, and evidence checks. Apache-2.0 weights. Text-only. Choice and yes/no inputs. Model card: huggingface.co/autotrust/Aut…

Try JEV in the browser. ⚡ AutoTrust AI’s JEV Demo is now live on @ModelScope2022 Studios — a hands-on place to explore our multimodal decision-model work. JEV is designed for the small but critical choices inside AI systems: selecting an option, assigning a score, evaluating confidence, and deciding what an agent should do next. Open the demo, try it with your own prompts, and follow AutoTrust Labs on ModelScope for new models, demos, and decision-AI updates. JEV Demo: modelscope.ai/studios/AutoTr…

Meet AutoTrust-JEV-27B v1.1 — a 27B text decision model built for the thousands of small choices inside an AI agent. Instead of generating a long response, it turns a defined question and its options into probabilities you can act on: choose the right tool, route an out-of-scope request, classify an intent, assess a claim, or select the next API action. In our complete local run of the Decision Index 0.3 public suite, JEV-27B v1.1 achieved a 67.31 public index across 37 benchmarks and 140,178 completed scoreable requests, with particular strength in tools and automation: • 96.25 on tool selection • 97.06 on intent classification with out-of-scope detection • 93.63 on banking intent classification • 78.24 on claim verification Apache-2.0 weights and decision readout are available now on @HuggingModels and @ModelScope2022. Download on Hugging Face: huggingface.co/autotrust/Aut… Download on ModelScope: modelscope.ai/models/AutoTru…

Adaptive Thinking, Measured. Thank you @HuggingModels for featuring AutoTrust AI’s GEV-26B-Decide. GEV is built for the decision layer inside agent products. System 1 makes a fast, calibrated choice: route a ticket, rank results, select a tool, or choose the next action. When confidence falls below 0.8, it can invoke System 2 reasoning and fold that result back into the final probabilities. The result: simple cases move quickly, while ambiguous cases get more thought instead of forcing a long reasoning trace on every request. In our 1,754-question public evaluation, adaptive mode reached 83.4% accuracy versus 83.8% with reasoning on every question, while calling System 2 for only 47.8% of cases. System 1 takes about 45 ms on one B200. The model card now shows 1.05M downloads in the past month. Download and build with it: huggingface.co/autotrust/GEV…