autotrust/JEV-35B
text-classification model by autotrust
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From the publisher
Model card & documentation
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[](https://news.guru.so/ui) Decision Index 0.3, public suite: 59.73 (our scoring with the kit), against 53.64 for autotrust/JEV-27B-VL on the board. Approximate public vision score 71.94 (our rebuild of the vision benchmarks; JEV-27B-VL 71.67 on the same rebuild, a tie within noise). Median single-request latency 204 ms on one B200 (JEV-27B-VL 271 ms). 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…
Read the full model card ↗ · Preview checked 2026-10-30T04:39:29.291Z
Inside the original model card — Document outline
- autotrust/JEV-35B
- Blog One Decision Model Instead of a Ranking Team
- 🔴 Live: AutoTrust/GuruSearch recommendation & search demo → news.guru.so
- A System 1 decision model on Qwen3.5-35B-A3B (35 B parameters, about 3 B active per token), up to 25
- 🤖 Robot embodied reasoning: ERQA 51.3% in one pass, zero robot training
- Decision Index 0.3 (public suite)
- Every benchmark
- Images (approximate public vision score)
- Speed
- Computer use, robot arm and games
- Quick start (vLLM)
- System 1: POST /v1/decide
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Documentation belongs to its respective authors. Reported project/model license: apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
Model overview
autotrust/JEV-35B is a text-classification 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
- text-classification
- Library
- transformers
- Recent downloads (30 days)
- 301,213
- Cumulative likes
- 2,703
- Hugging Face trending score
- 15
- Reported safetensors parameters
- 35,951,822,704
- Architecture
- Qwen3_5MoeForConditionalGeneration
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-10-11T01:22:03.000Z
- Last modified
- 2026-10-14T06:16:34.000Z
Compatibility and lineage
Reported languages: en
Reported base models: Qwen/Qwen3.5-35B-A3B
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 2026-10-19T06:43:12.148Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.