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/JEV-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV-9B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/JEV-9B") model = AutoModelForCausalLM.from_pretrained("autotrust/JEV-9B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Model card: ModelScope section — links only (no recommendation or install lines)
Browse files
README.md
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**https://www.modelscope.ai/models/AutoTrust-Labs/JEV-9B**
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AutoTrust Labs has an official homepage on ModelScope — https://www.modelscope.ai/organization/AutoTrust-Labs — which
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mirrors our Hugging Face repositories and syncs updates in real time. ModelScope has recommended our models on its
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website and on its WeChat (微信) and Xiaohongshu (小红书) accounts.
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```bash
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pip install modelscope
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modelscope download AutoTrust-Labs/JEV-9B --local_dir JEV-9B
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```
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**https://www.modelscope.ai/models/AutoTrust-Labs/JEV-9B**
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AutoTrust Labs has an official homepage on ModelScope — https://www.modelscope.ai/organization/AutoTrust-Labs — which
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website and on its WeChat (微信) and Xiaohongshu (小红书) accounts.
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