Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
system-two
blocks-of-experts
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
vllm
multimodal
vision
computer-use
robotics
Eval Results (legacy)
Instructions to use autotrust/AutoTrust-Decider-Mini-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/AutoTrust-Decider-Mini-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/AutoTrust-Decider-Mini-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/AutoTrust-Decider-Mini-9B") model = AutoModelForCausalLM.from_pretrained("autotrust/AutoTrust-Decider-Mini-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: news search powered by autotrust/JEV (news.guru.so)
Browse files
README.md
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> [!TIP]
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> **New (11 October 2026):
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> news or ask a question
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> answered by [autotrust/JEV-27B](https://huggingface.co/autotrust/JEV-27B), using its calibrated
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### AutoTrust's first integrated System 1 + System 2 open model, built with the Blocks of Experts recipe
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> [!TIP]
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> **New (11 October 2026): news search powered by autotrust/JEV is live at [news.guru.so](https://news.guru.so/ui).**
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> Search the news or ask a question: results from Google News, Bing News, Yahoo News, BBC, Al Jazeera and NPR are
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> ranked and answered by **[autotrust/JEV-27B](https://huggingface.co/autotrust/JEV-27B)**, using its calibrated
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> System 1 decisions.
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### AutoTrust's first integrated System 1 + System 2 open model, built with the Blocks of Experts recipe
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