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: 2,567 Bytes
b2f3bf4 b16c3a6 b2f3bf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | #!/usr/bin/env python3
"""Upload an export bundle (+ model card) to the Hugging Face Hub.
python3 scripts/push_hf.py --export exports/jev-judge-qwen35-9b --repo autotrust/JEV --dry-run
python3 scripts/push_hf.py --export exports/jev-judge-qwen35-9b --repo autotrust/JEV --private
"""
from __future__ import annotations
import argparse
import os
import yaml
def validate_card(path: str) -> dict:
text = open(path, encoding="utf-8").read()
if not text.startswith("---\n"):
raise SystemExit("README.md has no YAML front matter")
fm = text.split("---\n", 2)[1]
meta = yaml.safe_load(fm)
for k in ("license", "base_model", "library_name", "pipeline_tag", "tags"):
if k not in meta:
raise SystemExit(f"model card front matter missing `{k}`")
return meta
def main() -> None:
ap = argparse.ArgumentParser()
ap.add_argument("--export", required=True)
ap.add_argument("--repo", required=True, help="e.g. autotrust/JEV")
ap.add_argument("--private", action="store_true")
ap.add_argument("--dry-run", action="store_true")
ap.add_argument("--message", default="Upload JEV v0.7.0 (Qwen3.5-9B distilled from Jev 1.13)")
args = ap.parse_args()
card = os.path.join(args.export, "README.md")
meta = validate_card(card)
files = sorted(os.listdir(args.export))
total = sum(os.path.getsize(os.path.join(args.export, f)) for f in files)
print(f"repo: {args.repo} ({'private' if args.private else 'public'})")
print(f"card front matter: license={meta['license']} base_model={meta['base_model']} pipeline_tag={meta['pipeline_tag']} tags={len(meta['tags'])}")
print(f"files ({total/1e9:.2f} GB):")
for f in files:
print(f" {os.path.getsize(os.path.join(args.export, f))/1e9:8.3f} GB {f}")
if args.dry_run:
print("dry run — nothing uploaded")
return
from huggingface_hub import HfApi
api = HfApi()
who = api.whoami()
print("authenticated as", who.get("name"), "orgs:", [o.get("name") for o in who.get("orgs", [])])
api.create_repo(args.repo, repo_type="model", private=args.private, exist_ok=True)
url = api.upload_folder(repo_id=args.repo, repo_type="model", folder_path=args.export, commit_message=args.message,
ignore_patterns=["*.log", "__pycache__"],
delete_patterns=["model-*.safetensors", "model.safetensors.index.json", "code/**"])
print("uploaded ->", url)
print(f"https://huggingface.co/{args.repo}")
if __name__ == "__main__":
main()
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