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
Download vocab.json from autotrust/JEV-35B: direct link, hf CLI and curl.
- Browser
- Download file 6.72 MB
-
https://huggingface.co/autotrust/JEV-35B/resolve/main/vocab.json
- Command line
-
hf download hf://autotrust/JEV-35B/vocab.json
-
curl -L -o vocab.json https://huggingface.co/autotrust/JEV-35B/resolve/main/vocab.json
6.72 MB
File too large to display, you can check the raw version instead.