Text Classification
Transformers
Safetensors
English
gemma4
image-text-to-text
system-one
system-two
adaptive-thinking
typed-decisions
decision-model
calibrated-probabilities
jev
noul
choice
score
lora
mixture-of-experts
multimodal
vllm
Eval Results (legacy)
Eval Results
Instructions to use autotrust/GEV-26B-Decide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/GEV-26B-Decide with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/GEV-26B-Decide")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("autotrust/GEV-26B-Decide") model = AutoModelForMultimodalLM.from_pretrained("autotrust/GEV-26B-Decide", 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/GEV-26B-Decide
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### A decision model with adaptive thinking, on Gemma-4-26B-A4B-it (26 B parameters, ≈ 4 B active per token) — Decision Index 0.2.1: **62.48**
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## Decision Index
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# autotrust/GEV-26B-Decide
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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 decision model with adaptive thinking, on Gemma-4-26B-A4B-it (26 B parameters, ≈ 4 B active per token) — Decision Index 0.2.1: **62.48**
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## Decision Index
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