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
File size: 921 Bytes
aa7758f | 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 | #!/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()
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