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
| from __future__ import annotations | |
| import tempfile | |
| import unittest | |
| from pathlib import Path | |
| from qprefer_reward.scorer import _required_adapter_file | |
| class ArtifactResolutionTest(unittest.TestCase): | |
| def test_local_required_file_is_returned(self) -> None: | |
| with tempfile.TemporaryDirectory() as directory: | |
| expected = Path(directory) / "special_token_embeddings.safetensors" | |
| expected.write_bytes(b"fixture") | |
| observed = _required_adapter_file( | |
| directory, | |
| "special_token_embeddings.safetensors", | |
| None, | |
| ) | |
| self.assertEqual(observed, expected) | |
| def test_incomplete_local_artifact_fails_fast(self) -> None: | |
| with ( | |
| tempfile.TemporaryDirectory() as directory, | |
| self.assertRaisesRegex(FileNotFoundError, "Upload the complete"), | |
| ): | |
| _required_adapter_file( | |
| directory, | |
| "special_token_embeddings.safetensors", | |
| None, | |
| ) | |
| if __name__ == "__main__": | |
| unittest.main() | |