Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
system-one
typed-decisions
decision-model
calibrated-probabilities
knowledge-distillation
jev
noul
choice
score
lora
qwen3_5
dual-head
vllm
Eval Results (legacy)
Instructions to use autotrust/JEV with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autotrust/JEV with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autotrust/JEV")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("autotrust/JEV") model = AutoModelForCausalLM.from_pretrained("autotrust/JEV", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 400 Bytes
b2f3bf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import os
import sys
import pytest
ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.insert(0, os.path.join(ROOT, "src"))
def pytest_addoption(parser):
parser.addoption("--model-path", default=os.environ.get("JEV_MODEL_PATH", "/root/models/Qwen3.5-9B"))
@pytest.fixture(scope="session")
def model_path(request):
return request.config.getoption("--model-path")
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