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
MLX builds for Apple Silicon (8-bit, 4-bit and 3-bit mixed, with vision)
#3
by Paris99 - opened
Thanks for releasing JEV-35B under Apache-2.0. We've published MLX builds for Macs:
- netprojects/JEV-35B-mlx-8bit (36 GB)
- netprojects/JEV-35B-mlx-4bit-mixed (21 GB: experts 4-bit, other layers 8-bit)
- netprojects/JEV-35B-mlx-3bit-mixed (17 GB: experts 3-bit, other layers 8-bit)
All three keep the vision tower and router gates in bf16. jev35b-mlx reproduces the System 1 read-out of your serve_decide.py (slot logits + head bias, per-kind temperature from calibration.json), as a Python decide() and the same /v1/decide endpoint.
Against the original bf16 weights, on 231 decisions from the JevBench public set, the builds pick the same answer on 228 / 225 / 224 of 231 (mean KL 0.002 / 0.004 / 0.012). On an M5 Max a warm decision takes about 100–165 ms.
Happy to adjust anything if you'd like the read-out handled differently.
Paris Michalas, NetProjects