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
| #!/usr/bin/env python3 | |
| """Train S1/S2/S3 (DESIGN §6.4) on one GPU. | |
| python3 scripts/train.py --config configs/train.yaml --stage s2 --seed 42 --out checkpoints/s2_seed42 | |
| python3 scripts/train.py --config configs/train.yaml --stage s2 --subset-frac 0.1 --out checkpoints/s2_scan_a --set lambda_rps=1.0 | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import os | |
| import sys | |
| sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "src")) | |
| from jev_judge.train_loop import TrainConfig, Trainer # noqa: E402 | |
| def parse_set(items: list[str]) -> dict: | |
| out = {} | |
| for it in items or []: | |
| k, v = it.split("=", 1) | |
| try: | |
| import json | |
| out[k] = json.loads(v) | |
| except Exception: | |
| out[k] = v | |
| return out | |
| def main() -> None: | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--config", default="configs/train.yaml") | |
| ap.add_argument("--stage", choices=["s1", "s2", "s3"]) | |
| ap.add_argument("--seed", type=int) | |
| ap.add_argument("--out", dest="out_dir") | |
| ap.add_argument("--model", dest="model_path") | |
| ap.add_argument("--subset-frac", dest="subset_frac", type=float) | |
| ap.add_argument("--epochs", type=int) | |
| ap.add_argument("--max-steps", dest="max_steps", type=int) | |
| ap.add_argument("--init-from", dest="init_from") | |
| ap.add_argument("--set", nargs="*", help="extra overrides key=value (JSON values)") | |
| args = ap.parse_args() | |
| overrides = {k: v for k, v in vars(args).items() if k not in ("config", "set")} | |
| overrides.update(parse_set(args.set)) | |
| cfg = TrainConfig.from_yaml(args.config, overrides) | |
| Trainer(cfg).fit() | |
| if __name__ == "__main__": | |
| main() | |