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
Download reports/vl/text_parity.json from autotrust/JEV-9B: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
-
https://huggingface.co/autotrust/JEV-9B/resolve/main/reports/vl/text_parity.json
- Command line
-
hf download hf://autotrust/JEV-9B/reports/vl/text_parity.json
-
curl -L -o text_parity.json https://huggingface.co/autotrust/JEV-9B/resolve/main/reports/vl/text_parity.json
204 Bytes
| { | |
| "n": 300, | |
| "mean_abs_dp_max": 0.002224256388519142, | |
| "max_abs_dp": 0.010940762902599044, | |
| "share_over_0.05": 0.0, | |
| "same_top_option": 0.99, | |
| "kinds": { | |
| "noul": 140, | |
| "choice": 83, | |
| "score": 77 | |
| } | |
| } |