Instructions to use qgfvadfuvads/Q-Prefer-D2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use qgfvadfuvads/Q-Prefer-D2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Instruct") model = PeftModel.from_pretrained(base_model, "qgfvadfuvads/Q-Prefer-D2") - Notebooks
- Google Colab
- Kaggle
| #!/usr/bin/env python3 | |
| """Validate a Q-Prefer training manifest and print its immutable summary.""" | |
| from __future__ import annotations | |
| import argparse | |
| import json | |
| from pathlib import Path | |
| from qprefer_reward.training.data import load_manifest, parse_path_prefix_maps | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument("manifest", type=Path) | |
| parser.add_argument("--media-root", type=Path) | |
| parser.add_argument("--path-prefix-map", action="append", default=[], metavar="OLD=NEW") | |
| parser.add_argument("--check-media", action="store_true") | |
| args = parser.parse_args() | |
| _, summary = load_manifest( | |
| args.manifest, | |
| media_root=args.media_root, | |
| path_prefix_maps=parse_path_prefix_maps(args.path_prefix_map), | |
| check_media=args.check_media, | |
| ) | |
| print(json.dumps(summary, indent=2)) | |
| if __name__ == "__main__": | |
| main() | |