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/JEV-35B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV-35B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV-35B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("autotrust/JEV-35B") model = AutoModelForMultimodalLM.from_pretrained("autotrust/JEV-35B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# autotrust/JEV-35B
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## Blog [One Decision Model Instead of a Ranking Team(https://huggingface.co/blog/autotrust/one-decision-model-instead-of-a-ranking-team-short)
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[](https://news.guru.so/ui)
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# autotrust/JEV-35B
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## Blog [One Decision Model Instead of a Ranking Team](https://huggingface.co/blog/autotrust/one-decision-model-instead-of-a-ranking-team-short)
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[](https://news.guru.so/ui)
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