Text Classification
Transformers
Safetensors
English
qwen3_5_moe
image-text-to-text
system-one
typed-decisions
decision-model
calibrated-probabilities
jev
noul
choice
score
mixture-of-experts
vllm
Eval Results (legacy)
Eval Results
Instructions to use autotrust/AutoTrust-Decider-Pro-35B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/AutoTrust-Decider-Pro-35B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/AutoTrust-Decider-Pro-35B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("autotrust/AutoTrust-Decider-Pro-35B") model = AutoModelForMultimodalLM.from_pretrained("autotrust/AutoTrust-Decider-Pro-35B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: JEV-powered news search is live (news.guru.so)
Browse files
README.md
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# autotrust/JEV-35B
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### A System 1 decision model on Qwen3.5-35B-A3B (35 B parameters, about 3 B active per token), up to 256 options in one pass
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**Decision Index 0.3, public suite: 59.75** (our scoring with the kit), against 53.64 for
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# autotrust/JEV-35B
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> [!TIP]
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> **New (11 October 2026): JEV-powered news search is live at [news.guru.so](https://news.guru.so/ui).** Search the
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> news or ask a question; results from Google News, Bing News, Yahoo News, BBC, Al Jazeera and NPR are ranked and
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> answered by [autotrust/JEV-27B](https://huggingface.co/autotrust/JEV-27B), using its calibrated System 1 decisions.
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### A System 1 decision model on Qwen3.5-35B-A3B (35 B parameters, about 3 B active per token), up to 256 options in one pass
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**Decision Index 0.3, public suite: 59.75** (our scoring with the kit), against 53.64 for
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