Text Classification
Transformers
Safetensors
English
Ukrainian
qwen3_5
image-text-to-text
openjudgement
judgment
classification
structured-output
preview
custom-code
Instructions to use kitaniai/OpenJudgement-4B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kitaniai/OpenJudgement-4B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kitaniai/OpenJudgement-4B-Preview")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("kitaniai/OpenJudgement-4B-Preview") model = AutoModelForMultimodalLM.from_pretrained("kitaniai/OpenJudgement-4B-Preview", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """Run all three judgment types. Execute from anywhere after installing requirements.""" | |
| import argparse | |
| import json | |
| import sys | |
| from pathlib import Path | |
| ROOT = Path(__file__).resolve().parents[1] | |
| sys.path.insert(0, str(ROOT)) | |
| from openjudgement import OpenJudgement | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--model', default=str(ROOT)) | |
| parser.add_argument('--device', default='cuda') | |
| parser.add_argument('--request', type=Path, default=ROOT/'examples/request.json') | |
| args = parser.parse_args() | |
| request = json.loads(args.request.read_text()) | |
| judge = OpenJudgement.from_pretrained(args.model, device=args.device) | |
| result = judge.system_one(request['state'], request['questions']) | |
| print(json.dumps(result, indent=2, ensure_ascii=False)) | |
| if __name__ == '__main__': | |
| main() | |