autotrust/AutoTrust-Decider-Pro-Vision-27B
image-text-to-text model by autotrust
From the publisher
Model card & documentation
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[](https://news.guru.so/ui) The benchmark. ERQA (Embodied Reasoning Question Answering) is the benchmark Google DeepMind released with Gemini Robotics (March 2025) to measure whether a vision-language model understands the physical world a robot acts in. It has 400 multiple-choice questions about real robot and everyday scenes, with 1–16 images per question (287 questions have one image). 382 questions have 4 options and the rest 2–3, so chance is about 26%. ERQA is one of the standard embodied-reasoning benchmarks; Gemini Robotics-ER, PhysBrain and Hy-Embodied report it. With 400 questions the 95% interval of a score is about ±5 points. How we test. Each question is one choice decision through POST /v1/decide: the images, the question and the options A–D; the answer is the most probable option, so no question is lost to answer formatting. One pass = System 1 only. Thinking on = thinking: "on" for every question: System 2 (the unmodified base…
Read the full model card ↗ · Preview checked 2026-11-11T09:41:57.090Z
Inside the original model card — Document outline
- autotrust/JEV-27B-VL
- Blog One Decision Model Instead of a Ranking Team
- 🔴 Live: AutoTrust/GuruSearch recommendation & search demo → news.guru.so
- 🤖 Robot embodied reasoning: ERQA 61.5% with thinking, the top open model, zero robot training
- #1 on the Decision Index Vision board
- What the board shows
- JEV-27B that can see: the same System 1 decisions and System 2, now on images
- New (3 October 2026): robot arm and computer use
- New (1 October 2026)
- Highlight: zero-shot short-video recommendation (for TikTok-style feeds)
- Watch it decide
- Highlight: agent judge
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Model overview
autotrust/AutoTrust-Decider-Pro-Vision-27B is a image-text-to-text model repository published by autotrust on Hugging Face. The source reports the transformers library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- image-text-to-text
- Library
- transformers
- Recent downloads (30 days)
- 1,512,222
- Cumulative likes
- 4,796
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 27,781,427,952
- Architecture
- Qwen3_5ForConditionalGeneration
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-09-30T05:02:23.000Z
- Last modified
- 2026-10-14T06:16:37.000Z
Compatibility and lineage
Reported languages: en
Reported base models: Qwen/Qwen3.8-27B
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-11-14T06:59:45.906Z. Daily imports are snapshots, not real-time monitoring.
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